Question about how Mellea differs from other prompt-engineering libraries

[Kirushikesh]

It looks very interesting! I’m curious about how Mellea compares (and differentiates) from libraries like DSPy or Promptify, which focus on templating and structured prompt creation, while frameworks like LangChain handle workflow orchestration, chaining, and integration aspects.

Given that these already cover different sides of prompt engineering, I’m curious — what specific gaps or needs does Mellea aim to fill between (or beyond) these two categories?
In other words:

What core pain points in existing libraries motivated Mellea’s creation?
How does Mellea’s approach to “generative programming” provide a fundamentally different or more scalable alternative?
In practical use, when should someone pick Mellea over using Promptify for templating or LangChain for orchestration?

Apologies if it's evident from the docs.