Problem solved

AI-generated code poses significant security risks because it can contain malicious instructions, infinite loops, or resource exhaustion attacks. Developers integrating LLMs into applications need a way to execute model outputs safely without endangering the host system. AgentRun solves this by providing a lightweight sandboxed execution environment that restricts the capabilities of untrusted code, allowing safe and controlled execution.

Use cases

• Running code generated by AI coding assistants (like GitHub Copilot or ChatGPT) in a safe environment.
• Building autonomous AI agents that can execute Python code as part of a reasoning loop, with protection against unintended side effects.
• Educational platforms where students can submit Python code that is executed securely without risking server integrity.
• Prototyping and testing LLM-generated scripts for data analysis or automation without manual review.
• Implementing a secure code interpreter for chatbots that allows users to run Python calculations or visualizations.

Audience

• Developers
• ML Engineers
• Data Scientists
• Researchers

Pros

• Easy to integrate with existing Python codebases via a simple API.
• Strong security with multiple isolation backends (native or Docker).
• Configurable resource limits prevent denial-of-service attacks.
• Open source and actively maintained with frequent updates.
• Supports both local and remote execution through optional API server.

Cons

• Sandboxing may not be fully bulletproof against kernel-level exploits.
• Limited to Python code execution; other languages not supported.
• Docker-based isolation adds overhead and requires Docker installation.