[fnikol]

Indeed both frameworks support model chaining.

However, achieving goals like "training your LLM" or enabling "real-time inference" requires more than just pipelines. For that, we have invested in enhancing compatibility with databases and facilitating parallel computing.

About your last point, I 'm not sure I fully understand. Do you mean to write a guide for moving lang-chain models to superduper? Or to create superduper wrappers for langchain ? Or to move the core functionalities of superduper to langchain ?

The guide, is something have in our immediate plans. The wrappers are under discussion. The latter I don't think it's possible due to architectural differences. For example, superduper is designed with multi-node environments in mind.