Running LLM-Generated Code Safely: LangChain + Daytona Demo

Juraj Sulimanovic, a software engineer at Devōt, presents a proof-of-concept using Daytona and LangChain to safely generate and execute Python code with large language models (LLMs), addressing the inherent risks of unpredictability when running LLM-generated code in production environments. The demonstration showcases the creation of secure, isolated sandboxes provided by Daytona, where code is generated using LangChain and OpenAI, executed, and tested within a controlled environment. This approach not only ensures security but also allows for test-driven development (TDD) with AI, enabling automatic validation and iterative improvement of code through a self-healing mechanism that learns from test results. The sandbox environment supports various operations, such as code execution, file management, and test automation, making it a versatile solution for developing AI-assisted tools while maintaining safety and reliability.

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