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, developers can now choose from seven officially integrated 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. Selecting the right secure sandbox platform determines whether your agents can execute code safely, scale without manual intervention, and access GPU acceleration when workloads require it. This guide examines the seven hosted sandbox provider integrations announced for the OpenAI Agents SDK, starting with Modal, a serverless compute platform built for secure code execution at massive scale with on-demand GPU support.
Key Takeaways
- Native sandbox execution simplifies agent development: The OpenAI Agents SDK now provides sandbox execution in the Python SDK, with TypeScript support planned, eliminating the need to piece together custom isolation layers
- GPU support differentiates Modal from other providers: Modal is the only official sandbox provider offering GPU acceleration for ML workloads, enabling agents to run inference and fine-tuning tasks alongside code execution
- Security isolation is critical for AI-generated code: Agents generate and run code autonomously, making sandboxed execution essential. Modal uses gVisor containers while others like Vercel employ Firecracker microVMs
- Fast cold starts enable responsive agents: Modal achieves fast cold starts through memory snapshotting and an optimized filesystem, supporting 50,000+ concurrent sessions critical for agents handling dynamic workloads
- Code-first SDKs accelerate development: Modal's code-defined SDK supports Python, TypeScript, and Go, with no YAML required, enabling faster iteration compared to configuration-heavy alternatives
1. Modal
Modal delivers serverless compute purpose-built for AI workloads, offering secure sandboxes that can scale to massive concurrency with on-demand GPU access layered on top. The platform takes your code, containerizes it, and executes it in the cloud with automatic scaling, all defined through a code-defined SDK supporting Python, TypeScript, and Go.
Core Capabilities
- gVisor container isolation: Secure sandboxed execution for running AI-generated code with compute jobs containerized and virtualized using gVisor
- Massive concurrency: Support for 50,000+ concurrent sessions, enabling agents to handle dynamic workloads at scale
- Fast cold starts: Engineered for fast cold starts and faster feedback loops, with an optimized filesystem that helps containers come online quickly without letting large images slow startup down
- Code-first SDK: Modal is code-defined and supports SDKs in Python, TypeScript, and Go, with no YAML required. Functions and classes use decorators, while Sandboxes are created and configured programmatically with
Sandbox.create, and Images are defined through Modal's image APIs - On-demand GPU access: Agents can access GPUs including T4, L4, A10, L40S, A100 variants, H100, H200, and B200 when workloads require acceleration
Security and Compliance
Modal maintains SOC 2 Type II certification and supports HIPAA-compliant workloads on Enterprise plans via a BAA. The platform uses gVisor-based sandboxing for compute isolation, TLS 1.3 for public APIs, and encryption for data in transit and at rest.
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.
What Makes Modal Unique
- AI-native container runtime: Custom-built infrastructure including file system, container runtime, scheduler, and image builder optimized for AI workloads
- Memory snapshotting: Modal Memory Snapshots can reduce initialization-heavy Function cold starts, often by 3-10x. CPU Memory Snapshots are available for Functions; GPU Memory Snapshots and Sandbox memory snapshots are available in Alpha
- Multi-cloud capacity pool: Modal pools GPU capacity across multiple clouds to improve availability and provide reliable access to the latest GPUs without users managing quotas or reservations
- Production-proven at scale: Powers cloud infrastructure for over 10,000 teams, including production AI coding agents. Ramp, for example, uses Modal Sandboxes to power background coding agents that generate code changes and write them back into commits and pull requests
Best For: Teams building AI agents that need secure code execution at scale, with on-demand GPU access when workloads call for ML inference, model fine-tuning, or compute-intensive analysis.
2. E2B
E2B specializes in cloud development environments for AI agents, focusing on ephemeral code execution with Firecracker microVM isolation. The platform provides an open-source option for teams with specific data sovereignty requirements.
Core Capabilities
- Firecracker microVMs: Hardware-level isolation for running untrusted AI-generated code
- MCP gateway built-in: Native Model Context Protocol integration for connecting external tools like Browserbase and Exa
- Extended timeouts: E2B's OpenAI Agents SDK examples configure a 900-second timeout for long-running multi-step agent workflows
- SSH access: Debug live sandbox environments directly via terminal
Integration Features
E2B provides the E2BSandboxClient class with four complete code examples in the official documentation, including a fullstack code review example with parallel sandbox workers. The template marketplace offers pre-configured environments for Node.js, Python data science, and other common use cases.
Best For: Teams building coding agents that need MCP integration for external tool connectivity and prefer longer timeouts for complex multi-step workflows.
3. Cloudflare Sandbox
Cloudflare Sandbox runs on Cloudflare's global edge network, offering low-latency code execution with seamless integration into the Workers ecosystem. The platform provides an official 20-minute tutorial with a deploy button for setup.
Core Capabilities
- Global edge network: Sandboxes run on Cloudflare's worldwide infrastructure for low latency across regions
- Pre-configured runtimes: Ready for Node.js and Python development out of the box
- Workers ecosystem integration: Seamless connection with R2 storage, KV, and other Cloudflare products
- PTY sessions via WebSocket: Real-time interactive terminal access for debugging agent behavior
Setup and Deployment
Cloudflare provides a streamlined setup flow, including an official 20-minute tutorial with a deploy button. The sandbox bridge Worker architecture enables HTTP access and R2/S3 bucket mounts for persistent data.
Best For: Teams already using Cloudflare services who need global edge distribution for their coding agents.
4. Daytona
Daytona provides full-featured cloud development environments with a detailed seven-section integration guide covering basic to advanced patterns.
Core Capabilities
- Pause/resume functionality: Daytona supports filesystem persistence for stopped sandboxes; its pause feature, which can preserve filesystem and memory state via VM-based runners, is documented as experimental and requires contacting support for access
- Memory persistence across sessions: Daytona's OpenAI Agents guide demonstrates a memory capability that persists durable facts and preferences in structured files across sessions; low-level VM memory persistence depends on Daytona's pause/snapshot behavior
- Workspace persistence: Daytona supports filesystem persistence across stopped sandbox sessions and provides snapshot and archive mechanisms for maintaining state
- Custom capabilities support: Domain-specific tools can be added to sandboxes
Unique Features
Daytona's OpenAI Agents guide demonstrates memory consolidation as a background task with phase-1 and phase-2 processing, allowing agents to extract durable facts and preferences into structured files that persist across sessions.
Best For: Teams building agents that require persistent development environments with memory continuity and prefer workspace state that survives interruptions.
5. Vercel Sandbox
Vercel Sandbox provides Firecracker microVM isolation, the same VM technology used by AWS Lambda, for maximum security when running untrusted agent-generated code.
Core Capabilities
- Firecracker isolation: Each sandbox runs in an isolated microVM with no access to host filesystem, credentials, or network
- Automatic preview URLs: Exposed ports automatically get public URLs for testing agent-generated web applications
- Multi-runtime support: Vercel's current Sandbox supports node24, node22, and python3.13 runtimes, with node24 as the default; its OpenAI Agents SDK KB includes examples using python3.12 and node22
- Strong security model: Firecracker microVM isolation with a separate filesystem and network; the sandbox has no access to the host filesystem, credentials, or network
Use Case Focus
Vercel Sandbox excels for agents building or testing web applications, particularly Next.js projects. The automatic preview URL generation enables human operators to review agent-generated applications before deployment.
Best For: Teams building agents that generate web applications, particularly those working in the Next.js ecosystem who prioritize strong security isolation.
6. Blaxel
Blaxel offers a sandbox platform built specifically for AI agents, with a focus on persistent "agent computers" that stay on standby and resume quickly when needed.
Core Capabilities
- Template marketplace: Pre-built sandbox images including Next.js and Python, along with JavaScript/frontend, browser automation, Docker-in-Docker, and desktop environments
- Real-time preview URLs: Human operators can preview agent-generated applications in real-time during execution
- Expiration policies: TTL-based auto-cleanup for sandbox lifecycle management
- File transfer support: Strong examples of reading generated files from sandbox to host
Documentation Structure
Blaxel provides three progressive examples, simple agent, data analysis, and coding agent, with clear guidance on file transfer patterns and preview URL handling.
Best For: Teams building coding agents that generate web applications and need preview URL support with diverse template options.
7. Runloop
Runloop provides a devbox-based architecture with tunnel networking support, backed by isolated VM-style development environments.
Core Capabilities
- Devbox model: Architecture centered around persistent, isolated VM-style development environments
- Tunnel networking: Devboxes support tunnels for network access to running sandboxes
- Official SDK integration:
RunloopSandboxClient class provided in the OpenAI Agents SDK
Integration Status
Runloop is included in the official SDK providers table and was mentioned in the April 2026 announcement. The platform offers dedicated documentation for devbox management and tunnel configuration.
Best For: Teams with specific networking requirements who prefer a devbox-oriented architecture for sandbox execution.
Why Modal Stands Out for OpenAI Agents SDK Integration
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 are optimized for the unique demands of secure code execution, GPU-accelerated computation, and dynamic scaling that AI agents require.
The Only Sandbox Provider with GPU Support
Modal is the only official OpenAI Agents SDK sandbox provider offering GPU acceleration. While other providers focus exclusively on CPU-based code execution, Modal enables agents to access GPUs on-demand when workloads require ML inference, model fine-tuning, or compute-intensive analysis, a significant differentiator for AI-native applications.
Massive Concurrency with Fast Cold Starts
Modal's sandbox infrastructure supports 50,000+ concurrent sessions. Engineered for fast cold starts and faster feedback loops, Modal uses memory snapshotting and an optimized filesystem that helps containers come online quickly without letting large images slow startup down. This combination of scale and startup speed is essential for coding agents handling dynamic workloads where rapid container spin-up directly impacts user experience and agent responsiveness.
Enterprise Security and Compliance
With SOC 2 Type II certification, HIPAA-compliant workload support via BAA, and comprehensive security practices including gVisor sandboxing and TLS 1.3, Modal meets the compliance requirements that enterprise AI agent deployments demand.
Developer Experience Without Compromise
Modal's code-defined SDK supports Python, TypeScript, and Go, eliminating infrastructure configuration overhead. Functions and classes use decorators, while Sandboxes are created programmatically with Sandbox.create and Images are defined through Modal's image APIs, all without YAML. This approach enables rapid iteration and deployment velocity that configuration-heavy platforms struggle to match.
For teams building AI agents that require secure code execution, production-grade reliability, and on-demand GPU access, 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.
Explore the Modal documentation to get started building with the OpenAI Agents SDK.
View Modal Docs