Agent Sandbox
Agent Sandbox · Coding
Agent Sandbox is a secure code execution API that gives AI agents a real computer to work with. Instead of letting a language model run code on your own server, you send that code to an isolated sandbox, watch it run, and pull back whatever files it creates. It handles execution, file transfer, dependency installs, and artifact retrieval through a single API, and it plugs into frameworks like Claude Code, Codex, and Cursor with minimal setup. For teams that need reliable sandboxed code execution, it removes the job of standing up your own containers.
About Agent Sandbox
What Is Agent Sandbox
Agent Sandbox is a runtime environment built for one specific problem: running code that an AI agent wrote but that you don't fully trust. Language models generate Python and shell commands confidently, and sometimes those commands are wrong, risky, or outright hostile. Running them next to your production data is a bad idea. Think about that for a second. One bad line of generated code can delete a folder or leak a key.
The product wraps each execution in its own isolated container. You upload files in, the sandbox runs the code, and you download the outputs. If the agent asks for a library the container doesn't have, you list the package and Agent Sandbox installs it for that run. The team behind it describes the service as containerized and Kubernetes-backed, which is what keeps one session from stepping on another.
The main limitation is money. There's no free forever tier. You get $10 in credits when you sign in with Google, roughly eleven hours of compute, and after that you pay per second of use. If you're prototyping something small, that's fine. If you want to run large jobs around the clock, the meter keeps running.
Getting Started
- Sign in to agentsandbox.co with your Google account. You'll receive $10 in starting credits and an API key.
- Install the SDK with
pip install agentsandbox-sdk, or call the REST endpoints athttps://api.agentsandbox.codirectly using your key as a Bearer token. - Send your first job: pass a Python or shell snippet to the execute endpoint and let the sandbox run it in an isolated container.
- If your code needs extra libraries, include them in the request and they get installed automatically for that run.
- Grab the artifacts. Charts, logs, and transformed datasets come back as downloadable files from the session.
Product Information
A quick look at Agent Sandbox's pricing, supported platforms, and performance.
Best for
The users, tasks, and scenarios where this tool fits best.
Users
- Developers building AI agents
- Backend engineers adding a code interpreter
- Solo builders and weekend projects
Tasks
- Running LLM-generated Python safely
- Building a data analyst feature
- Executing shell commands in a scratch workspace
Scenarios
- Agents that read and transform user documents
- Prototyping a coding assistant
- Automated report generation
Key features
Isolated code execution
Every job runs inside its own container, so one session can't reach another's files or processes. You send code, the sandbox runs it, and nothing touches your host. The isolation is the whole point: it's what makes running untrusted LLM output realistic instead of reckless. No isolation, no deal.
Single API for code, sessions, and artifacts
Agent Sandbox doubles as a turnkey code interpreter API. It puts execution, session management, and file handling behind one interface. Upload inputs, start a run, and download outputs without stitching together three services. For teams, that means less glue code to maintain and fewer places for something to break.
Automatic dependency installation
If your agent's code needs a library or a system tool, you list it in the request and Agent Sandbox installs it for that specific run. Installs stay reproducible, so the same request tends to behave the same way next time. No manual image rebuilds when your agent decides it needs pandas. That saves real time.
Files in, files out
The API moves data both directions. You upload datasets for the agent to work on, and you download everything it produces, including charts, logs, and transformed files. This turns a stateless model into something that can actually leave a deliverable behind. Files in, files out. Simple.
Framework-agnostic integration
You can use Agent Sandbox as a single tool inside just about any agent framework, client-side or server-side. The docs list Claude Code, Codex, Cursor, Windsurf, and Cline as compatible, and there's a Python SDK for direct use. You can wire it into a client-side script or a server-side pipeline, so the way you connect is your call rather than something the product forces on you. Pick how you want to connect rather than adapting to one rigid workflow.
Pay-per-second billing
Compute costs $0.00025 per second, billed by the second, and storage runs $0.0005 per MB for output files. There's no subscription and no minimum commitment. So why not just spin up your own containers? Because you'd pay for idle time too. You top up between $5 and $1,000 at a time and spend it as you go, which suits bursty workloads better than a monthly seat.
Usage dashboard
Your account shows balance, usage history, and spending patterns in one place. That matters when agent workloads are unpredictable, because you can spot a runaway loop before it drains your credits instead of after. Runaway loops are common. Watch the counter.
Pros and cons
Pros
- Isolation by default means you can run agent-generated code without exposing your own systems.
- One API covers execution, file transfer, and dependency installs, cutting down on integration work.
- Per-second pricing with no subscription fits intermittent or experimental projects.
- SDK plus REST access and support for several popular agent frameworks keep setup light.
- $10 in sign-in credits is enough to test a real proof of concept before paying.
Cons
- There's no free tier, so anything beyond the initial credits costs money, including quick experiments.
- Costs scale with usage, and a looping agent can burn through credits faster than you'd expect without a spending cap.
- Pricing is usage-based rather than a flat rate, which makes budgeting harder if you can't predict volume.
- The product is relatively new, so community documentation and third-party guides are still thin.
Frequently asked questions
It's a secure code execution API and a code interpreter API rolled into one. It runs code inside an isolated container and returns the results. You send a script, the sandbox executes it safely, and you download any files it produced. It's built for code that AI agents generate, where you want the output but not the risk of running it locally.
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