LangChain aims to connect language models to external data and computational sources, enhancing their ability to perform tasks like retrieval augmented generation, API interactions, and code generation, while acknowledging associated AI safety risks such as unsafe outputs or malicious activities like prompt injection. To address these concerns and facilitate innovation, LangChain has introduced a separate Python package, langchain_experimental, to house components with potential security vulnerabilities and experimental features, thus distinguishing them from the more stable core LangChain. This separation is intended to make core LangChain more robust for production use while allowing rapid development in langchain_experimental, with the team promising to inform users of any breaking changes with at least a week's notice and providing migration instructions. The LangChain team expresses gratitude towards the community and specific contributors for their support and patience during this transitional phase.