OpenShell 與 Docker Sandboxes 比較：如何選擇 AI 智能體運行環境？

An AI coding agent can read a repository, run shell commands, install packages, and send network requests. That is useful until a tool call reaches the wrong file, leaks a token, or downloads code that behaves differently than expected.

Two projects now aim to put a boundary around that work: NVIDIA OpenShell and Docker Sandboxes. They solve related problems, but they are not interchangeable products. OpenShell is a policy-driven runtime and control plane for governing agent access across sandboxes. Docker Sandboxes is a developer-facing way to run coding agents in isolated local or cloud environments.

The right comparison is not "which sandbox is more secure?" Both vendors describe substantial isolation controls, and documentation alone cannot prove that one is safer in every configuration. The practical question is which workflow matches your environment, how much policy control you need, and what you can test yourself.

This comparison is based on the projects' current public documentation and repositories, not a hands-on benchmark. Product features and support details can change, so check the linked docs before a deployment.

The short answer

Choose OpenShell when you need explicit policy for files, processes, network destinations, API methods, and credential use, especially across multiple runtimes or a Kubernetes deployment. Its core design separates the untrusted agent workload from a trusted supervisor that makes policy decisions.

Choose Docker Sandboxes when the immediate goal is to run a supported coding agent in an isolated developer workflow, with local and cloud options, an sbx CLI, and integrations for common agents. Docker documents sandbox lifecycle, workspace mounts, cloud execution, and agent-specific setup.

Choose neither by default if a regular container with narrowly scoped permissions already meets the threat model. A new runtime also adds operational work. The boundary only helps when it is configured, monitored, and tested against the actions the agent can take.

These tools sit at different layers

A sandbox is an isolated place where an agent can run. A control plane manages sandboxes and the rules they follow. Some products combine both ideas, but their emphasis differs.

OpenShell describes itself as a runtime for fleets of autonomous agents. Its gateway manages sandbox lifecycle, policies, providers, credentials, logs, and access. Each workload is paired with a trusted supervisor. The agent runs on the untrusted side; the supervisor checks requests and supplies credentials only when policy allows.

Docker Sandboxes focuses on running AI coding agents in isolated environments. Its documentation describes local sandboxes and Docker-managed cloud sandboxes, a CLI for creating and managing them, and guides for specific agents such as Claude Code, Codex, Copilot, Cursor, and OpenCode.

That difference shapes the decision. OpenShell starts with "What may this agent access, and how do we enforce that policy?" Docker Sandboxes starts with "How can a developer run this coding agent in a separate environment?" The boundaries can overlap, but those starting points are not the same.

How their security models differ

OpenShell mediates access through a supervisor. Its architecture documentation says the sandbox's only permitted network path is to the supervisor. The supervisor evaluates network requests against policy, resolves destinations, and adds provider credentials only for approved endpoints. OpenShell also describes filesystem and process rules, plus a policy prover that checks proposed network-policy changes and can flag risky access for human review.

The agent does not receive the provider credential as an ordinary environment variable. That design can reduce exposure if the agent process or its workspace is compromised. It does not remove the need to scope credentials, review policies, protect the gateway, and check the behavior of the whole deployment.

Docker Sandboxes emphasizes isolated agent workspaces. The product supports local and cloud sandbox workflows, and its docs cover workspace mounting, agent integration, credentials, network policy, and lifecycle management. A sandbox can persist between sessions or be removed after a run, depending on how it is started and managed.

The user still needs to understand what the agent can reach and what data is mounted into the environment. For example, Docker's usage documentation says sbx run mounts the current directory when no workspace path is supplied. That is convenient, but it means the selected working directory is part of the agent's accessible context. Review the mount and credential behavior before running an agent against a sensitive checkout.

These are descriptions of documented designs, not independent security certifications. An isolation feature can be misconfigured, and a product's threat model may not match yours. Test the actual rules and failure behavior rather than relying on the feature list.

A practical comparison

Policy depth: OpenShell exposes policy concepts for filesystem access, processes, network destinations, API calls, and where provider credentials may be used. Docker Sandboxes documents controls around sandbox configuration, networking, credentials, and governance. If you need an explicit policy model that follows the agent across different runtimes, OpenShell's design is more directly aimed at that problem.

Developer workflow: Docker Sandboxes has an sbx command-line workflow and documentation for multiple coding agents. That can make it easier to introduce into an existing local development routine. OpenShell can run agents too, but its documentation presents a broader gateway, supervisor, and policy workflow.

Deployment choices: OpenShell documents Docker, Podman, Kubernetes, and MicroVM runtimes. Its support matrix lists Linux and Apple Silicon macOS as supported host platforms, with Windows on WSL 2 marked experimental. Docker Sandboxes documents local execution and a Docker-managed cloud option. Confirm the current requirements for your operating system and desired deployment before choosing.

Credential handling: OpenShell's architecture says provider credentials stay with the trusted supervisor and are added only to approved requests. Docker documents credential configuration for its sandbox environments. Do not infer that credentials are invisible to an agent just because they are configured through a product interface; verify the exact mechanism and test what the process can read.

Operational scope: OpenShell introduces a gateway and policy administration model, which may fit platform teams that run many agent workloads. Docker Sandboxes presents a closer fit for developers who want to isolate individual coding sessions. Neither description means one cannot be adapted to the other's use case. It means the initial operational model is different.

Maturity signals: OpenShell's current repository documents stable 0.1.x releases and recommends stable builds for production. That is an early release line, so check the release and compatibility policy carefully. Docker's docs list broad agent integrations and local and cloud workflows. Documentation breadth is useful, but it is not a substitute for your own test of support, reliability, and security boundaries.

How to run a fair evaluation

Do not compare a carefully restricted OpenShell setup with a permissive Docker configuration, or the reverse. Use the same agent, model, repository, task, and credentials. Then test what the agent can and cannot do.

Start with a disposable repository that contains no real credentials or private customer data. Give the agent a task that requires reading and changing one project file. Then test the boundary deliberately:

Ask it to read a file outside the workspace.
Ask it to contact a host that should not be allowed.
Try to access a credential from the process or a child command.
Attempt to reach a local service or cloud metadata address that should be blocked.
Stop or disconnect the policy component and observe whether the workload fails closed.
Review logs to see which action was allowed, blocked, or not recorded.

For each runtime, write down the expected result before the test. A test is useful only when you know what should happen. Repeat after changing policy, upgrading the runtime, or changing the underlying Docker, Kubernetes, or host configuration.

Measure developer friction too. Record setup time, startup time, how often a legitimate request needs approval, what happens when the network is unavailable, and how easy it is to remove a sandbox and its data. The fastest first run is not always the easiest system to operate safely over time.

A decision checklist

Use this short checklist before adopting either tool:

Where will agents run? One developer laptop, a shared build environment, or a Kubernetes fleet?
What can an agent touch? A single repository, mounted host folders, internal services, or cloud APIs?
How are credentials handled? Can the agent read them directly, or are they mediated at the boundary?
How precise must network rules be? Do you need destination and API-method policies, or does a simpler allowlist suffice?
Who owns policy changes? One developer, a platform team, or a security team with review requirements?
Can you test fail-closed behavior? What happens when a gateway, supervisor, or network policy component becomes unavailable?
What is the lifecycle? Are sandboxes persistent, cleaned up after a task, and covered by backup and retention rules?
What is the rollback path? Can you return to the existing workflow if the pilot blocks legitimate work?

If the main need is a safer local coding-agent workflow, try Docker Sandboxes first with a disposable project and minimal access. If the main need is policy-driven governance across a fleet, evaluate OpenShell's gateway and policy model. If neither improves the current controls enough to justify its complexity, keep the simpler setup and narrow the agent's permissions.

The takeaway

OpenShell and Docker Sandboxes both respond to a real shift: coding agents need access to files, processes, and networks, so running them with broad host access is a poor default. OpenShell's distinctive focus is a policy-enforcing supervisor and control plane. Docker Sandboxes' distinctive focus is an accessible isolated workflow for coding agents, including local and cloud execution.

There is no winner based on a feature list alone. Pick the tool whose operating model fits the people who will configure it, then test its real boundary with a harmless project. The valuable result is not a sandbox label. It is evidence that the agent can complete useful work while blocked from actions it should not take.

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Have you tried either OpenShell or Docker Sandboxes? Which matters more in your environment: policy control across a fleet, or a simple isolated workflow for each developer?