Production LangChain, shipped in 14 days.
A thin layer beats a deep one
LangChain is useful for the parts it makes genuinely simpler: standard chains, common integrations, a quick prototype. It becomes a liability when a deep abstraction hides the one behavior you need to debug at 2am. We use it where it clarifies and we drop to plain code where it obscures.
If you arrive with a LangChain codebase that has become hard to reason about, a common sprint is to keep what helps and replace the rest with explicit, readable orchestration you can actually trace.
The discipline does not change
Whatever the framework, an AI feature still needs a bounded job for the model, a confidence gate, and an eval harness. LangChain does not remove that work. We do it the same way we always do.
What a LangChain sprint typically covers
- A new feature built on your existing LangChain stack.
- A refactor to thin out abstractions that have become hard to debug.
- An eval harness retrofitted onto a LangChain pipeline.
- A retrieval layer rebuilt for cost and traceability.
LangChain questions.
Sometimes. It helps for standard chains and quick work. We are honest when a thinner, explicit layer would be easier to debug and cheaper to run.
A LangChain feature to ship?
Send a one-page brief. A fixed price and a ship date back by morning.