llm-sandbox: A Container-Backed Runtime for LLM-Generated Code
Lightweight and portable LLM sandbox runtime (code interpreter) Python library.
At a glance
- What is it?
- vndee/llm-sandbox wraps Docker, Kubernetes or Podman in a Python context manager so generated code runs outside your host process. It is a thin orchestration layer, not a security boundary, and the README is clearer about backends than about threat models.
- Who is it for?
- Adopt llm-sandbox if you already run Docker or Podman somewhere in your stack and want a Python context manager that turns generated code into a container run with library installation and artifact capture attached. Do not adopt it if you need a hard multi-tenant isolation guarantee, or if you have no container runtime to point it at, since the library orchestrates containers rather than replacing them.
- Can I use it commercially?
- Yes. MIT is a permissive licence: you can use, modify and sell software built on it, as long as you keep its copyright and licence notices.
- Is it still maintained?
- Yes. The repository last received commits 2 days ago.
- What is it written in?
- Mainly Python, according to GitHub's language statistics.
Answers come from the project's GitHub data, last synced on September 15, 2026, and from our analysis. They are not legal advice.
DEEP OPEN-SOURCE ANALYSIS
The gap llm-sandbox fills between a model and a running process
An agent that writes Python and then executes it inside the same interpreter as your application has handed the model your process. Environment variables, mounted volumes and open sockets are all in scope. The obvious fix is to run the code somewhere else, but wiring that up means image selection, dependency installation, file transfer and output capture, repeated for every language you support. llm-sandbox packages that repetition into a session object.
The intended user is a developer building an agent, a code interpreter feature or an evaluation harness in Python, who is willing to run containers and does not want to write the container plumbing. The README frames the goal as running LLM-generated code "in a safe and isolated mode" and lists isolation, security policies, resource limits and network isolation as the first feature group. Those are properties of the container runtime underneath, which matters for how you read the rest of the documentation.
SandboxSession, backends and what actually executes the code
The visible architecture is a session object over a container backend. `SandboxSession` is constructed with a language and optional image, used as a context manager, and exposes a `run` method that returns a result with at least a `stdout` attribute. The README's first example prints `result.stdout` after running a two-line Python program. There is no interpreter embedded in the library; the code string is shipped to a container and executed there.
Three backends are documented: Docker, described as the most popular and widely supported option, Kubernetes for orchestration at scale, and Podman for rootless containers. Which one you get is decided by the extra you install and, presumably, by session configuration, though the README shows no explicit backend parameter in its examples. Language support is handled by images. Python, JavaScript, Java, C++, Go and R are listed, and the R example passes an explicit image, `ghcr.io/vndee/sandbox-r-451-bullseye`, which suggests non-default languages may require you to name an image rather than rely on a built-in default.
Two features sit on top of the run loop. `libraries` is a per-call list that triggers dependency installation before execution, shown with `numpy` for Python and `axios` for JavaScript. Artifact extraction captures plots and visualizations, and separate file operations copy files in and out of the sandbox. Container pooling pre-warms containers for reuse, and a fast production mode skips environment setup to shorten startup. Both are described as performance measures, which tells you container startup and dependency installation are the cost centres the project has chosen to attack.
Installing the backend you actually have
The base install is `pip install llm-sandbox`, which the README presents without a backend. Backend support is opt-in through extras: `pip install 'llm-sandbox[docker]'`, `pip install 'llm-sandbox[k8s]'` or `pip install 'llm-sandbox[podman]'`, and all three can be combined as `pip install 'llm-sandbox[docker,k8s,podman]'`. If you install the base package and then construct a session expecting Docker, the extra is the first thing to check.
For contributors, dev dependencies live in a uv dependency group. The documented path is `git clone https://github.com/vndee/llm-sandbox.git`, then `cd llm-sandbox`, then `make install`, which the README describes as `uv sync` plus `pre-commit install`. CONTRIBUTING.md is pointed to for the full workflow.
The runtime surface shown in examples is small: the constructor takes `lang`, optionally `image` and `verbose`, and `run` takes the code string plus an optional `libraries` list. No configuration file, no environment variable and no global setup step appears in the README. That is a genuine advantage for embedding the library in an existing service, and it also means anything not shown in an example is something you will have to read the documentation site for.
What the README does not settle about isolation
The security claims are the weakest part of the supplied material. "Isolated Execution" is described as code running in isolated containers with no access to the host system, and "Network Isolation" as the ability to control network access for sandboxed code. Neither section shows the parameter, default or configuration key that produces those states. Resource limits for CPU, memory and execution time are listed the same way.
This is not a reason to assume the features are absent. It is a reason to treat them as unverified until you read the documentation site or the source. A container with the default network mode has network access, and a container run without explicit CPU and memory caps can consume what the host allows. Whether llm-sandbox applies restrictive defaults or leaves them to you is exactly the question a security review needs answered, and the README as supplied does not answer it.
The second limitation is structural. A container boundary is not the same as a hostile-code boundary. Kernel-level container escapes are a known class of issue, and a sandbox that shares a kernel with your application is weaker than a virtual machine boundary. If the code you are executing comes from an untrusted third party rather than from a model you control, the container backend is a starting point, not the whole answer.
Third, the project is versioned in the 0.3.x line, with three releases within roughly a day in early August 2026 according to the release list. Rapid patch releases at that cadence usually mean active bug fixing, which can also mean API churn. Pin the version.
Where a full runtime beats a library, and where it does not
The closest alternative in kind is a hosted code execution service: you send code over HTTP and get stdout, stderr and files back, and someone else operates the isolation layer. The difference in approach is where the container lives. llm-sandbox runs the container on infrastructure you already control, which means no per-execution vendor cost, no code leaving your network, and no dependency on a third party's uptime. The trade is that you now own image maintenance, container runtime upgrades and the capacity planning that the pooling feature hints at.
A second alternative is writing the container call yourself with the Docker SDK or the `docker run` CLI. For a single language and a fixed image, that is perhaps thirty lines, and you avoid a dependency entirely. llm-sandbox earns its place when you need more than one language, on-the-fly library installation, artifact capture and file transfer, since those are the parts that multiply as you add languages. If you only ever run Python against one pinned image, the library is convenience rather than necessity.
A third option, for pure Python, is a restricted execution environment inside the process. That avoids container startup latency completely, but it shares your interpreter and is the situation the sandbox exists to avoid. It is the right tool only when the code is trusted.
Maintenance cost, versioning and the MIT licence
The library is MIT licensed, which permits commercial and closed-source use with the usual requirement to retain the copyright notice and licence text. That is a permissive baseline, and it applies to llm-sandbox itself. It does not automatically cover the container images you run. The README names `ghcr.io/vndee/sandbox-r-451-bullseye` as an example image, and images bundle language runtimes and system packages under their own licences. If you build and distribute a product around a bundled image, check that image's terms separately. Nothing here is legal advice.
Upgrade cost is shaped by the 0.3.x cadence. The public surface shown in the README is narrow (a constructor, a `run` method, a `libraries` argument), so the blast radius of a minor bump is likely small, but the release history shows several releases close together, which is the pattern you see when fixes are shipping faster than the API is settling. Pin an exact version in your requirements and treat upgrades as a deliberate step.
The ongoing operational cost is the container runtime. Docker, Kubernetes and Podman all need patching, and the images your sessions pull need rebuilding when base images get security updates. llm-sandbox does not remove that work. It moves the work from writing execution plumbing to maintaining the infrastructure the plumbing depends on, which is a better trade only if you were going to run containers anyway.
Who should adopt llm-sandbox, and what to check first
The fit is a Python team building an agent or code-interpreter feature that already operates Docker, Podman or Kubernetes, needs more than one language, and wants dependency installation and artifact capture handled by the library rather than by hand. The eleven agent framework examples under `examples/agent_sdks/`, covering OpenAI Agents SDK, Claude Agent SDK, LangChain, LlamaIndex, CrewAI and others, give you a starting point that matches whatever orchestration layer you already use, and the MCP server support means an MCP client such as Claude Desktop can drive the sandbox directly.
The poor fit is a team with no container runtime, a team that needs a hard isolation guarantee against adversarial code rather than model-generated code, or an application where per-execution container startup latency is unacceptable and pooling has not been evaluated. For a single-language, single-image use case, the Docker SDK is a smaller dependency.
Three things to verify before adopting. First, read the documentation site for the security policy, resource limit and network isolation configuration, since the README lists those features without showing the keys. Second, confirm the image for your target language exists and is maintained, using the R example as the pattern for how non-default languages are configured. Third, measure container startup and dependency installation in your own environment, because fast production mode and container pooling are the project's stated answers to that cost and you need to know whether they are enough for your latency budget.
Editorial conclusion
Adopt llm-sandbox if you already run Docker or Podman somewhere in your stack and want a Python context manager that turns generated code into a container run with library installation and artifact capture attached. Do not adopt it if you need a hard multi-tenant isolation guarantee, or if you have no container runtime to point it at, since the library orchestrates containers rather than replacing them. Before committing, verify which backend extra matches your infrastructure, confirm the base image for your target language exists, and check whether your deployment can tolerate the container startup cost that the documented fast production mode and container pooling are meant to reduce.
Community notes