Many AI agents need access to computing environments.
They write and run code, install packages, clone repositories, analyze files, start development servers, and interact with operating-system environments. As agents take on more autonomous work, allowing them to execute code directly inside an application's production infrastructure can create significant security and reliability risks.
To support those workflows safely, developers need isolated environments where agents can execute commands, access a filesystem, use network resources, and complete longer-running tasks without exposing the host application.
There are several ways to provide that infrastructure. Some platforms are built specifically around sandboxes for AI agents. Others offer sandbox execution as part of a broader serverless compute platform, while a third category integrates isolated environments into an existing application or cloud ecosystem.
Rather than attempting to catalog every remote execution service available today, this guide focuses on six of the leading platforms developers are using to give AI agents secure computing environments. While the list is not exhaustive, it covers many of the architectural approaches teams are likely to evaluate.
By the end of this roundup, you'll understand where each platform excels, the tradeoffs behind its design, and the types of AI applications it is best suited to support.
What Is an AI Agent Sandbox Platform?
An AI agent sandbox platform gives agents an isolated environment where they can safely execute code and interact with computing resources.
That isolation matters because agents don't always know in advance whether the code they generate or select will behave as intended. It may contain bugs, consume excessive resources, access files it should not see, or attempt network operations the application did not intend.
Running that code directly on the host application can expose production systems, credentials, internal data, and other workloads. A sandbox creates a boundary between the agent's execution environment and the rest of the application.
Inside that boundary, an agent may be able to run shell commands, install dependencies, create and modify files, clone repositories, execute programs, expose development servers, and preserve state between tasks. The exact capabilities depend on the platform.
Not every sandbox occupies the same architectural layer.
Purpose-built agent sandbox platforms provide APIs and SDKs specifically for creating and controlling agent environments. Broader compute platforms expose sandboxes alongside functions, containers, GPUs, storage, and other infrastructure. Application-platform sandboxes connect isolated execution more closely to an existing deployment ecosystem.
In most systems, the language model and agent orchestration still run outside the sandbox. The sandbox becomes a tool the agent can invoke whenever it needs to execute code or work inside a computer environment. In other architectures, the agent itself may run inside the sandbox so that its entire working environment remains isolated.
How to Evaluate an AI Agent Sandbox Platform
Most sandbox platforms can run a command in an isolated environment. The more important differences emerge when agents need to maintain state, execute long-running tasks, access external resources, or create large numbers of environments in production.
Every application has different requirements, but we have found that these five considerations provide a useful starting point.
Isolation Model
The first question is how the platform separates agent workloads from the rest of your infrastructure.
Different platforms use containers, microVMs, full virtual machines, or combinations of those technologies. The implementation affects security boundaries, startup behavior, operating-system compatibility, and the types of workloads the sandbox can support.
Developers should also consider how the platform handles secrets, processes, filesystem access, and communication between sandboxes. Strong isolation reduces the risk that untrusted code can affect the host application or another user's environment.
Environment Lifecycle and Persistence
Some agent tasks finish in seconds. Others continue across many interactions or require the agent to return to an environment later.
Ephemeral sandboxes work well for isolated code execution, testing, and short-lived analysis. Stateful environments are more useful for coding agents, research workflows, and other applications where files, installed packages, and intermediate work need to survive between sessions.
Snapshots, reusable images, suspend-and-resume support, and persistent storage can reduce repeated setup work. The right lifecycle model depends on whether each task should start clean or continue from an earlier state.
Startup Speed and Scalability
Sandbox startup time directly affects how quickly an agent can begin working.
For interactive products, even a modest delay may become noticeable to users. Applications that create many environments simultaneously also need to consider concurrency limits, provisioning throughput, and how efficiently the platform scales workloads up and down.
Fast startup is not the only consideration. A platform must also maintain reliable execution as the number and duration of agent sessions increase.
Runtime and Resource Flexibility
Different agents need different environments.
A lightweight data-analysis agent may only require Python and a small amount of memory. A coding agent may need a full Linux environment, package installation, Git, background processes, and exposed ports. Other workloads may require custom images, Docker, GPUs, private networking, or more substantial CPU and memory allocations.
The broader the application's requirements, the more important runtime customization and resource selection become.
Developer Experience
Sandbox infrastructure sits inside a larger agent application.
Developers should consider how easily they can create environments, execute commands, stream output, transfer files, manage processes, configure networking, create snapshots, and clean up resources through the platform's SDKs and APIs.
Language support and framework integrations also matter. A platform that fits naturally into the application's existing stack can reduce the amount of lifecycle management and infrastructure code the team needs to build.
The 6 Best AI Agent Sandbox Platforms
1. Daytona
Best for: Teams building stateful coding agents and workflows that need persistent, configurable development environments.
Daytona provides isolated, programmatically managed sandboxes that function as full, composable computers for AI agents. Developers can control sandbox lifecycles, files, Git operations, processes, code execution, terminal sessions, and computer-use capabilities through its APIs, CLI, and SDKs.
The platform also supports Windows and GPU sandboxes. In addition, customers can choose between microVM- and Docker-based isolation models depending on their preferences and workload requirements.
The platform places particular emphasis on stateful agent workflows. Sandboxes can be created from snapshots containing an operating system, installed packages, dependencies, and configuration, allowing agents to begin from a prepared environment rather than installing the same tooling repeatedly.
Daytona also supports persistent volumes that exist independently of an individual sandbox and can be mounted by multiple environments. This is useful when agents need to share datasets, dependencies, repositories, or other files across tasks without copying them into each new sandbox.
Its documentation includes integrations and implementation guides for agent frameworks and coding tools, including Mastra, the OpenAI Agents SDK, LangChain, LangGraph, Vercel AI SDK, and several coding-agent systems. That makes Daytona particularly useful for teams that want sandbox infrastructure designed to connect with a wider agent-development stack.
Why You Might Choose Daytona
- Designed around stateful infrastructure for AI agents.
- Supports filesystem, Git, process, terminal, and code-execution operations.
- Uses snapshots to create reusable, preconfigured environments.
- Supports Windows and GPU sandboxes, with both microVM- and Docker-based sandbox options.
- Provides persistent volumes that can be shared across sandboxes.
- Offers integrations and guides for many agent frameworks.
- Strong fit for coding agents and longer-running development workflows.
Potential Tradeoffs
Daytona offers a broad set of environment and lifecycle capabilities, which may be more infrastructure than applications need for short, stateless code execution. Teams should evaluate its resource model and configuration options against the size and duration of their expected workloads.
2. E2B
Best for: Teams that want purpose-built sandbox infrastructure for coding agents, data analysis, and computer-use applications.
E2B provides isolated cloud environments that allow AI agents to execute code, process data, run tools, and interact with a filesystem through JavaScript, TypeScript, and Python SDKs. Its sandboxes can provide agents with terminal, filesystem, Git, and network access without requiring developers to run generated code inside their own application infrastructure.
The platform is designed specifically around AI agent workloads. Developers can use E2B for code interpretation, software-engineering agents, automated testing, and other applications where a model needs access to a general-purpose computing environment.
E2B also supports reusable templates and snapshots. Its snapshots can capture both the filesystem and memory state of a running sandbox, allowing developers to start additional environments from an existing point in time rather than rebuilding the environment for every task.
Beyond command-line code execution, E2B offers desktop sandboxes for computer-use agents that need to see and control a virtual Linux desktop. That gives it a broader scope than platforms focused only on running individual code snippets.
Why You Might Choose E2B
- Built specifically for AI agents and applications.
- Provides terminal, filesystem, Git, and network access.
- Supports JavaScript, TypeScript, and Python SDKs.
- Useful for coding agents and AI-generated data analysis.
- Offers reusable sandbox templates and snapshots.
- Supports virtual desktop environments for computer-use agents.
Potential Tradeoffs
E2B is focused on supplying isolated computers for agents rather than providing a complete agent framework or broader general-purpose cloud platform. Teams will still need to manage models, orchestration, memory, evaluations, and application logic elsewhere in their stack.
3. Modal
Best for: Teams that want sandbox execution alongside scalable compute, custom environments, and broader AI infrastructure.
Modal is a serverless compute platform that includes Sandboxes for executing untrusted user or agent code inside secure containers. Developers can create sandboxes programmatically, run commands, access files, configure resources, and connect sandbox execution with the rest of Modal's infrastructure.
Unlike platforms focused primarily on agent environments, Modal supports a broader range of compute workloads. Teams can use the same platform for serverless functions, batch processing, model inference, scheduled jobs, storage, notebooks, and other AI infrastructure.
That broader architecture makes Modal particularly useful when sandbox execution is only one part of a compute-intensive application. For example, an agent might execute generated code in a sandbox and then use other Modal resources for model serving or parallel processing.
Modal supports custom Images and filesystem snapshots that can be used to start new sandboxes from a prepared environment. It also offers a next-generation sandbox backend for applications requiring higher creation throughput and large numbers of concurrent sandboxes, although that backend is currently labeled beta.
By default, Modal Sandboxes do not accept incoming network connections or have access to other Modal resources, giving teams a secure starting point from which they can configure the access their workload requires.
Why You Might Choose Modal
- Combines sandbox execution with a broader serverless compute platform.
- Supports custom Images and configurable CPU and memory resources.
- Provides command execution and filesystem access.
- Offers filesystem snapshots for reusable environments.
- Useful for compute-intensive and highly parallel AI workloads.
- Strong fit when sandboxing is one component of a larger Modal architecture.
Potential Tradeoffs
Modal is broader than a purpose-built agent sandbox provider. For teams primarily looking to provide agents with isolated execution environments, it's important to understand how Sandboxes integrate with Modal's broader compute platform and infrastructure primitives.
4. Vercel Sandbox
Best for: Teams building AI applications on Vercel that need persistent Linux environments for code execution, testing, and agent workflows.
Vercel Sandbox is an isolated compute primitive for safely running untrusted or user-generated code. It provides Linux environments that developers can create through JavaScript and Python SDKs or a command-line interface, then use to run commands, manage files, expose ports, and control network access.
Sandboxes provide full root access and support package installation, external API calls, and custom runtime images. Vercel also supports container engines, VPN clients, and FUSE filesystems inside compatible sandbox runtimes, making the environments useful for more complete development and testing workflows.
Persistence is enabled by default. When a persistent sandbox stops, Vercel saves its filesystem state and restores it when the environment resumes. Teams can also create one-off ephemeral sandboxes when a workflow should start clean and be discarded afterward.
The strongest architectural advantage is its connection to Vercel's application platform. Teams already deploying AI applications through Vercel can use Sandbox alongside the company's hosting, functions, AI SDK, AI Gateway, and other platform services rather than introducing a separate infrastructure provider.
Why You Might Choose Vercel Sandbox
- Natural fit for applications already deployed on Vercel.
- Provides isolated Linux environments with root access.
- Supports JavaScript and Python SDKs.
- Offers persistent sandboxes by default.
- Can run commands, manage files, expose ports, and control network policy.
- Supports more complete Linux workflows, including compatible container and FUSE tooling.
Potential Tradeoffs
Vercel Sandbox is most naturally aligned with applications already using the Vercel ecosystem. Teams operating primarily on another cloud platform may prefer sandbox infrastructure that is less closely associated with a specific application deployment environment.
5. Cloudflare Sandbox SDK
Best for: Teams building on Cloudflare Workers that want isolated code execution connected to durable, stateful application logic.
Cloudflare Sandbox SDK allows developers to execute untrusted code inside isolated Linux environments from Cloudflare Workers applications. It provides APIs for running commands, managing files, starting background processes, streaming output, and exposing services.
The product combines three parts of Cloudflare's platform. Workers run the application logic, Durable Objects provide each sandbox with a persistent identity, and Containers supply the Linux environments where code executes.
Cloudflare Containers run each sandbox inside its own virtual machine, providing VM-level isolation between workloads. The platform also includes input validation and network controls intended to make untrusted execution safer.
The SDK is designed primarily for use from Workers. For applications running elsewhere, Cloudflare provides a reference sandbox bridge that exposes the SDK through a standard HTTP API, allowing other services and programming languages to create and control environments.
That architecture makes Cloudflare Sandbox SDK particularly useful when code execution needs to connect with Cloudflare's broader edge and application platform, including Workers and Durable Objects.
Why You Might Choose Cloudflare Sandbox SDK
- Integrates directly with Cloudflare Workers.
- Uses VM-isolated Containers for untrusted code execution.
- Supports commands, files, background processes, and exposed services.
- Uses Durable Objects to provide stateful sandbox identities.
- Can be exposed to external applications through the sandbox bridge.
- Strong fit for teams already building on Cloudflare's platform.
Potential Tradeoffs
The SDK is designed around Cloudflare Workers and is currently available on the Workers Paid plan. Applications outside the Workers ecosystem must add an HTTP bridge or another integration layer, which may make a platform with a direct standalone API simpler for some architectures.
6. Runloop
Best for: Teams building software-engineering agents that need secure, reusable development environments and integrated evaluation infrastructure.
Runloop provides secure sandboxed execution environments called Devboxes. These environments use virtual-machine technology to isolate agent code, credentials, secrets, data, and internal systems while giving agents access to a complete development environment.
Devboxes are designed primarily around software-engineering agents. Teams can create environments, execute code, interact through terminal sessions, mount repositories, and control Devboxes programmatically through Python and TypeScript SDKs or Runloop's CLI.
Runloop offers Blueprints for reproducibly building reusable environments and snapshots for capturing the disk state of a running Devbox. Teams can use snapshots to resume work later or create multiple environments from a known starting point. Devboxes can also be suspended and resumed rather than recreated for every interaction.
The platform also supports network policies that restrict the external hosts an environment can access. This allows developers to give agents access to approved services while limiting their ability to communicate with other network resources.
Beyond sandbox execution, Runloop includes infrastructure for evaluating and benchmarking software-engineering agents. That makes it especially relevant to teams that want to test agent performance across repeatable development tasks rather than using sandboxes only as runtime environments.
Why You Might Choose Runloop
- Built around software-engineering agents.
- Uses virtual machines to isolate agent environments.
- Provides reusable Blueprints and disk snapshots.
- Supports suspend-and-resume workflows.
- Can mount source-code repositories into prepared environments.
- Includes evaluation and benchmarking infrastructure for coding agents.
Potential Tradeoffs
Runloop is more specifically oriented toward software-engineering agents than some of the other platforms in this guide. Teams primarily running data analysis, general-purpose code interpretation, or non-development workloads may find that another sandbox platform aligns more directly with their use case.
Which AI Agent Sandbox Platform Should You Choose?
Choose Daytona if...
Your agents need stateful, configurable development environments with snapshots, persistent storage, and integrations across a broad range of agent frameworks.
Choose E2B if...
You want a purpose-built agent sandbox platform for coding agents, code interpretation, data analysis, or virtual computer-use environments.
Choose Modal if...
You want sandbox execution as part of a broader serverless compute platform that can also support model inference, parallel workloads, and other AI infrastructure.
Choose Vercel Sandbox if...
Your AI application already runs on Vercel and you want persistent Linux environments that fit naturally into the same application platform.
Choose Cloudflare Sandbox SDK if...
You are building with Cloudflare Workers and Durable Objects and want isolated code execution integrated with that architecture.
Choose Runloop if...
You are developing software-engineering agents and need reusable development environments alongside evaluation and benchmarking infrastructure.
Frequently Asked Questions
Why Do AI Agents Need Sandboxes?
Agents often need to execute code, install software, open files, or run shell commands to complete a task. Because agent-generated code may be incorrect, unpredictable, or unsafe, running it directly inside the host application can create security and reliability risks.
A sandbox isolates that execution from the rest of the application and limits the systems the code can affect.
What Is the Difference Between a Sandbox and a Container?
A container packages a process and its dependencies into an isolated environment, but the word "sandbox" describes the security and execution boundary presented to the application.
Sandbox platforms may use containers, microVMs, virtual machines, or combinations of these technologies underneath. The important question for developers is not only which technology is used, but how processes, files, networks, secrets, and users are isolated.
Should the Agent Run Inside or Outside the Sandbox?
Both architectures are possible.
Many applications run the agent and language model orchestration outside the sandbox, then expose sandbox operations as tools the agent can call. This keeps the application's control plane separate from the environment executing generated code.
Other applications place the agent itself inside the sandbox, particularly when it needs a complete computer environment or should remain isolated throughout a long-running task.
Do Sandboxes Preserve Files Between Agent Sessions?
Some do.
Ephemeral sandboxes discard the environment after a task ends. Stateful sandboxes may preserve files automatically, support suspend-and-resume behavior, mount persistent volumes, or allow developers to create snapshots.
Choose the model based on whether each task should begin in a clean environment or continue from previous work.
Are Sandboxes Only for Coding Agents?
No.
Coding agents are one of the clearest use cases, but sandboxes can also support data analysis, document processing, automated testing, research workflows, code interpretation, reinforcement-learning environments, and applications that let users generate and run software.
Some platforms also support virtual desktops for agents that need to interact with graphical applications.
Can an Agent Use More Than One Sandbox?
Yes.
An application can create a separate sandbox for each user, task, repository, agent, or stage of a workflow. It can also run many environments in parallel when tasks are independent.
The appropriate model depends on the application's isolation, persistence, concurrency, and cost requirements.
Should Every Task Start in a New Sandbox?
Not necessarily.
Starting with a clean sandbox reduces the risk that files or processes from one task affect another. It is often the safest approach for independent user requests or untrusted workloads.
Persistent environments are more effective when an agent is working on a long-running project, maintaining a repository, or repeatedly using the same packages and files. Snapshots can provide a middle ground by allowing new sandboxes to start from a trusted, preconfigured state.
Can Sandboxes Access the Internet?
Many sandbox platforms allow outbound network access, but the degree of control varies.
Some environments allow open internet access by default. Others begin with restricted networking or let developers define policies that limit the domains, services, or private networks a sandbox can reach.
Production applications should grant only the network access the agent actually needs.
Can I Switch Sandbox Platforms Later?
Yes, but the amount of work required depends on how tightly the application is coupled to a provider's SDK, lifecycle model, filesystem APIs, snapshot format, and networking controls.
Keeping sandbox operations behind an internal interface and separating application logic from provider-specific APIs can make future migration significantly easier.
