Secure sandboxing for AI agents and LLM code execution
Deterministic, hardened isolation. Run arbitrary code from AI models, agents, and untrusted sources. Know exactly what executes, before it does.
Get StartedLLMs and agents generate code. That code is often untrusted. Traditional sandboxes are slow, leak state, or require kernel modification. You need isolation that is:
Open-source sandboxing designed for AI. Built on proven isolation principles, auditable by your security team, deployable in your infrastructure.
Execution happens inside a sealed VM. No filesystem escapes. No kernel exploits. Code runs in a cage.
Same input, same output, every run. Perfect for testing, replaying errors, and audit trails.
Capture every syscall, memory operation, and I/O event. Build trace-based security checks on top.
Simple primitives: spawn sandbox, execute code, collect result. Works with Claude, GPT-4, open-source LLMs.
Low overhead. Scales horizontally. Works in containerized, serverless, and on-prem environments.
Open source on GitHub. Your security team can review, fork, harden. No black boxes.
Run AI-generated Python, JavaScript, or shell code safely. Capture output, errors, and traces. No sandbox breakout risk.
Let agents run tools - Python scripts, API calls, file operations - inside bulletproof isolation. Audit every action.
Run untrusted code with regulatory confidence. Deterministic traces satisfy SOC2, HIPAA, PCI-DSS audit requirements.
TakoVM is production-ready. Install from source or use prebuilt binaries.
git clone https://github.com/Tako-Research/TakoVM
cd TakoVM
make buildThen run your first sandboxed command:
takovm run "python -c 'print(1 + 1)'"TakoVM ships with:
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This is not a purchase and there is no card field. It puts your address, this product, and whatever you write below in front of a person, and you get a written answer about what finishing it, or handing it over for you to run yourself, would actually take.