llm-sandbox
LLM Sandbox is a Python library that safely executes code generated by large language models in isolated container environments. It supports multiple programming languages (Python, JavaScript, Java, C++, Go, R) and container backends (Docker, Kubernetes, Podman) with built-in resource limits and security policies.
Key facts
Objective fields from the source. Values we can't verify are shown as “Unknown” rather than guessed.
| Field | Value |
|---|---|
| Repository | vndee/llm-sandbox |
| Owner | vndee |
| Primary language | Python |
| License | MIT — OSI-approved |
| Stars | 1.1k |
| Forks | 103 |
| Open issues | 28 |
| Latest release | 0.3.39 (2026-04-20) |
| Last updated | 2026-05-05 |
| Source | https://github.com/vndee/llm-sandbox |
What llm-sandbox is
A lightweight sandbox runtime that provisions isolated containers to execute LLM-generated code with configurable resource constraints, network isolation, and multi-language support. Includes interactive session support via IPython kernels, artifact extraction for plots/visualizations, and integration with MCP protocol for Claude Desktop and other clients.
Get the llm-sandbox source
Clone the repository and explore it locally.
git clone https://github.com/vndee/llm-sandbox.gitcd llm-sandbox# follow the project's README for install & configurationNeed it deployed, integrated, or customized instead? DEV.co ships production installs.
Best use cases
Implementation considerations
- Container backend must be installed and running on the host; Docker is the easiest default, but Podman (rootless) or Kubernetes offer alternatives with different trade-offs.
- Resource limits (CPU, memory, execution time) should be configured based on expected workloads and host capacity; poorly tuned limits can cause job failures or resource starvation.
- Library/dependency installation happens at runtime per session; pre-build custom container images for production to avoid repeated build steps and latency.
- Error handling and exit code inspection are essential; the `result` object contains stdout, stderr, and exit codes—parse them to distinguish success, timeout, and failure modes.
- Interactive sessions (IPython kernel) maintain state across multiple `run()` calls but are only supported on Docker/Podman/K8s with Python; lifecycle management is critical to avoid stale kernels.
When to avoid it — and what to weigh
- Untrusted Code Execution Without Deep Audit — Do not use as a blanket solution for completely untrusted code without security hardening; container escapes and kernel exploits remain theoretical risks requiring careful threat modeling.
- Production Systems Without Container Orchestration — If you lack Docker, Podman, or Kubernetes infrastructure, setup and operational burden increases; requires container runtime expertise for production deployment.
- Real-Time, Ultra-Low-Latency Requirements — Container startup overhead (even with pooling) may not meet sub-millisecond latency demands; better suited for batch, interactive, or request-response workflows.
- Compliance Scenarios Requiring Formal Security Audits — No evidence of third-party security assessment, SOC 2, or penetration testing in provided data; organizations with strict compliance mandates should conduct independent evaluation.
License & commercial use
MIT License—permissive open-source license allowing unrestricted commercial use, modification, and redistribution with only attribution and liability disclaimer required.
MIT is a permissive OSI-approved license explicitly allowing commercial use. No registration, fees, or vendor approval needed. Review your internal compliance policies, but no license barrier to commercial deployment. Always retain original license headers and consider your own liability insurance if running untrusted code.
DEV.co evaluation signals
Editorial assessment — not user reviews. Directional, with an explicit confidence level.
| Signal | Assessment |
|---|---|
| Maintenance | Active |
| Documentation | Adequate |
| License clarity | Clear |
| Deployment complexity | Moderate |
| DEV.co fit | Strong |
| Assessment confidence | High |
Container isolation is the primary security boundary; escape risk depends on host kernel and container runtime versions (Docker/Podman/K8s security patches are critical). Network isolation and resource limits are configurable but require careful tuning. No mention of security audit, fuzzing, or formal threat model. Custom security policies are mentioned but documentation on policy DSL/examples is not in provided data. Assume container-escape and kernel-exploit scenarios are possible and plan accordingly (e.g., run on isolated VMs, restrict what code is submitted).
Alternatives to consider
E2B Code Interpreter
Managed cloud sandbox specifically for LLM code execution; eliminates container ops burden but adds cost and vendor lock-in compared to self-hosted llm-sandbox.
Docker/Podman Directly
Lower-level container management without LLM-specific ergonomics; more control but requires building your own sandboxing, resource management, and multi-language orchestration.
Modal / Brev.dev
Serverless execution platforms with built-in isolation and scaling; good for production workloads but less suited for interactive/iterative code execution than llm-sandbox.
Build on llm-sandbox with DEV.co software developers
Evaluate llm-sandbox for your AI agent or code execution service. Check GitHub docs, review security considerations, and test with your Docker/Kubernetes setup.
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llm-sandbox FAQ
Can I use llm-sandbox on Windows or macOS?
What happens if the sandboxed code runs forever or crashes?
How do I add custom Python packages or system libraries?
Is llm-sandbox suitable for a production AI agent or code execution service?
Custom software development services
Adopting llm-sandbox is usually one piece of a larger software development effort. As a software development agency, DEV.co provides software development services and web development expertise — pairing senior software developers and web developers with your team to design, build, and operate ai coding agents software in production.
Ready to Run LLM-Generated Code Safely?
Evaluate llm-sandbox for your AI agent or code execution service. Check GitHub docs, review security considerations, and test with your Docker/Kubernetes setup.