Best Code Execution Sandbox for OpenAI Agents SDK in 2026
AI agents that write and execute code autonomously need infrastructure that can handle untrusted code safely and scale on demand. With the April 2026 launch of native sandbox support in the OpenAI Agents SDK, OpenAI announced built-in support for seven hosted sandbox providers (Blaxel, Cloudflare, Daytona, E2B, Modal, Runloop, and Vercel) to run agent-generated code in isolated environments, alongside built-in SDK sandbox clients such as Docker and Unix-local environments. OpenAI's current sandbox documentation lists six of them as hosted clients, and Runloop documents its backend as an optional extra in the SDK. Selecting the right secure sandbox platform determines whether your agents can execute code safely, keep startup consistent as the number of parallel agent runs grows, and burst to the CPU and memory a task needs without overprovisioning every sandbox. This guide examines the seven hosted sandbox provider integrations announced for the OpenAI Agents SDK, starting with Modal, a scalable, high-performance sandbox platform for executing AI-generated code at production scale.
Key Takeaways
1. Modal
Modal is a scalable, high-performance sandbox platform for executing AI-generated code, built for teams running agents at production scale. Its secure sandboxes combine high concurrency, automatic CPU and memory bursting, and consistent fast starts under load, with on-demand GPU access when agent workloads call for it. Modal provides code-first SDKs in Python, TypeScript, and Go.
Core Capabilities
Security and Compliance
Modal maintains SOC 2 Type II certification and supports HIPAA-compliant workloads on Enterprise plans via a BAA. The platform uses VM and gVisor sandboxing for compute isolation, TLS 1.3 for public APIs, and encryption for data in transit and at rest. Modal supports region-pinning.
Integration with OpenAI Agents SDK
Modal's OpenAI Agents SDK integration uses Modal Sandboxes through the SDK's sandbox tooling. Modal's official guide, Building with Modal and the OpenAI Agents SDK, covers setup, GPU-enabled agent execution, and parallel subagents, referencing SandboxAgent, ShellTool, ModalSandboxSession, and ModalSandboxClientOptions. The platform supports Sandbox state persistence through filesystem snapshots, directory snapshots, memory snapshots, and persistent storage options such as Volumes or CloudBucketMounts.
Why Teams Choose Modal for OpenAI Agents SDK
2. E2B
E2B provides cloud sandboxes for AI agents, focused on ephemeral code execution with Firecracker microVM isolation. Its core is open source, and a self-hosting option is available.
Capabilities
Considerations
Why Teams Choose E2B
E2B is a dedicated sandbox provider with an open-source core and bring-your-own-cloud deployment, and it has run 1 billion sandboxes in total. Agents SDK teams choose E2B when they want a standalone sandbox point solution plugged in through E2BSandboxClient.
3. Cloudflare Sandbox
Cloudflare Sandbox provides code execution environments built on Cloudflare Workers, Durable Objects, and Containers, with each sandbox running as an isolated Linux container inside its own VM.
Capabilities
Considerations
Why Teams Choose Cloudflare
Cloudflare Sandbox comes as one piece of Cloudflare's developer platform, reached from Python agents through a bridge Worker. Teams that already run Workers choose it for the Agents SDK when they want sandboxes wired into their existing Cloudflare architecture.
4. Daytona
Daytona offers isolated sandboxes and development environments built around persistence, with a detailed seven-section OpenAI Agents SDK integration guide covering basic to advanced patterns.
Capabilities
Considerations
Why Teams Choose Daytona
Daytona is a dedicated sandbox provider with persistence built in and fast starts at lower scale. Agents SDK teams choose it as a point solution for stateful agents that carry memory from one run to the next when they don't need high concurrency.
5. Vercel Sandbox
Vercel Sandbox runs untrusted agent-generated code in Firecracker microVMs as part of the Vercel platform.
Capabilities
Considerations
Why Teams Choose Vercel
Vercel Sandbox sits inside Vercel's broader cloud platform rather than standing alone. Teams already deploying on Vercel pick it for Agents SDK workloads when they want CPU billed on active time and are fine with memory billed as provisioned.
6. Blaxel
Blaxel offers a sandbox platform built for AI agents, centered on persistent "agent computers" that stay on standby and resume when needed.
Capabilities
Considerations
Why Teams Choose Blaxel
Blaxel is a standalone sandbox service whose model revolves around keeping sandboxes on persistent standby. Agents SDK teams pick Blaxel when they want each agent's sandbox to idle on standby between runs and come back with its earlier state.
7. Runloop
Runloop provides a devbox-based architecture with tunnel networking support, backed by isolated VM-style development environments.
Capabilities
Considerations
Why Teams Choose Runloop
Runloop bundles benchmarking infrastructure with its sandboxes. Agents SDK teams choose Runloop when they want that benchmarking layer ready to use and are comfortable with environments set up like DevBoxes rather than short-lived sandboxes.
Why Modal Stands Out for OpenAI Agents SDK Integration
Massive Concurrency at Production Scale
Modal's sandbox infrastructure has demonstrated creating 1M+ sandboxes in under a minute, exceeding 10,000 creations per second, supports up to 1M concurrent sandboxes, and regularly runs tens of thousands of concurrent customer sandboxes. For teams fanning out parallel subagents with the Agents SDK, that headroom means a fleet can grow from a handful of sessions to many thousands without performance degradation.
CPU and Memory Bursting
An Agents SDK run is uneven by nature: the agent waits on the model, then ShellTool might list a directory in one step and compile a project or run a full test suite in the next. Fixed-reservation pricing forces teams to size every sandbox for the heaviest of those steps, so most of the reserved capacity goes unused and costs climb relative to the work done. With Modal, the CPU and memory set through ModalSandboxClientOptions act as a baseline, and sandboxes automatically burst above it when a step needs more, so teams can provision for typical usage instead of peak usage. Billing is per second at the greater of requested and actual resources, and bursting also lowers the risk of out-of-memory failures when a step outgrows its baseline.
Purpose-Built for AI Agent Workloads
Modal's architecture is specifically engineered for agentic and machine learning workloads. The platform's custom container runtime, scheduler, and file system keep sandbox starts sub-second and consistent under heavy concurrent load, including with large custom images that package an agent's dependencies and tools.
GPU Sandboxes When Agents Need Them
Most Agents SDK workloads run on CPU, but some agents need GPU acceleration for ML inference, model fine-tuning, or compute-intensive analysis. Modal's GPU sandboxes offer full sandbox functionality on 10+ GPU types, including B300, B200, H200, and H100, without reservations, and the same platform provides dedicated inference and training products when that work moves beyond the sandbox.
Enterprise Security and Compliance
With SOC 2 Type II certification, HIPAA-compliant workload support via BAA, region-pinning, and comprehensive security practices including VM and gVisor sandboxing and TLS 1.3, Modal meets the compliance requirements that enterprise AI agent deployments demand.
Code-First Developer Experience
Modal provides code-first SDKs in Python, TypeScript, and Go. Sandboxes are created programmatically with Sandbox.create, and the Agents SDK integration connects through ModalSandboxSession and ModalSandboxClientOptions, so teams configure sandboxes in the same code that defines their agents.
For teams building AI agents that require secure code execution at high concurrency, automatic CPU and memory bursting, and consistent startup under load, with GPU access when workloads call for it, Modal's combination of AI-native infrastructure, sandboxed execution at scale, and proven enterprise compliance makes it the clear choice for OpenAI Agents SDK integration.
A code execution sandbox is an isolated environment where AI-generated code runs without access to host systems, other workloads, or sensitive data. For OpenAI Agents that generate and execute code autonomously, sandboxing is critical, it prevents malicious or buggy generated code from causing damage. The OpenAI Agents SDK now provides native sandbox execution in both its Python and TypeScript SDKs, giving developers an execution layer without forcing them to piece it together themselves.
Sandbox providers use different isolation technologies to protect data. Modal offers VM and gVisor sandboxing where compute jobs are containerized and isolated, while Vercel and E2B employ Firecracker microVMs for hardware-level isolation. Sandboxing isolates code from host resources and other workloads; however, network access and egress controls vary by provider and should be configured according to your threat model.
For enterprise deployments, look for SOC 2 Type II certification, which validates security controls over time. Modal maintains SOC 2 Type II compliance, supports HIPAA-compliant workloads on Enterprise plans via a BAA, and offers region-pinning. Additional security features to evaluate include encryption in transit and at rest, TLS 1.3 for APIs, and documented vulnerability remediation timeframes by severity. Modal documents Critical at 24 hours, High at 1 week, Medium at 1 month, Low at 3 months, and Informational at 3 months or longer.
Yes. Modal pairs its sandboxes with dedicated inference and training products, so OpenAI Agents can call hosted models or kick off fine-tuning on the same platform that runs their code. Modal Sandboxes can also be configured with GPU support across 10+ GPU types, including B300, B200, H200, and H100, without reservations, letting agents run inference models, perform fine-tuning, or execute compute-intensive analysis alongside standard code execution when a task needs acceleration.
Modal's OpenAI Agents SDK integration uses Modal Sandboxes through the SDK's sandbox tooling, with SandboxAgent, ShellTool, ModalSandboxSession, and ModalSandboxClientOptions, and supports up to 1M concurrent sandboxes with sub-second starts that stay consistent under load. Sandboxes burst above their requested CPU and memory when an agent's commands spike, VM and gVisor isolation secures untrusted code execution, and on-demand GPU access enables agents to run ML workloads when they need it. Sandbox state persistence through filesystem snapshots, directory snapshots, memory snapshots, and storage options such as Volumes allows agents to maintain state across sessions, and Modal provides code-first SDKs in Python, TypeScript, and Go.
Ephemeral sandboxes spin up for a task and tear down afterward, ideal for stateless code execution. Persistent sandboxes maintain state across sessions. Modal supports Sandbox state persistence through filesystem snapshots, directory snapshots, memory snapshots, and persistent storage options such as Volumes or CloudBucketMounts. Modal Sandbox timeouts are configurable up to 24 hours, and longer work continues in a new Sandbox restored from a filesystem snapshot. The right choice depends on whether your agent needs clean-room execution each time or continuity across workflows.
Modal bills Agents SDK sandboxes on active resources: each sandbox requests a CPU and memory baseline, can burst above it automatically, and is charged per second for whichever is greater, the requested or the actual resources. That lets teams size sandboxes for the typical step in an agent run and still reach peak capacity when ShellTool launches a heavy build or test, and bursting cuts the chance of a run failing on an out-of-memory error. Most sandbox providers charge for a fixed reservation that can't be exceeded, so teams size every sandbox for its largest possible step and keep paying for headroom that sits idle through most of the run.
