LangChain development for production LLM apps

When LangChain helps and when it does not

A framework earns its place when it removes code you would otherwise write and test yourself. LangChain does that for model switching, structured output parsing, tool calling, retrieval wiring and, through LangGraph, state machines with checkpoints. It costs you extra layers to read when something breaks.

What a LangChain build usually contains

We have no published case study on LangChain, so here is the shape of work we scope rather than a client story. Most builds combine four pieces.

• A retrieval chain: loader, splitter, embedding model, vector store and a retriever with metadata filters.
• A tool layer: typed functions for lookups and actions, each with a short description the model can follow.
• A graph in LangGraph when the flow has loops, such as retry with a different query or ask a human to approve.
• Tracing and evaluation, so every run can be inspected step by step and replayed against a test set.

For simpler chat use cases see AI chatbot development. For agents with real tool access, see AI agent development.

Gotchas from real use of the framework

• Version churn. The project has split into several packages and renamed or moved classes more than once. We pin versions and read release notes before every upgrade.
• Abstraction leaks. When an answer is wrong, you often need to see the exact prompt sent to the model. We turn on tracing from day one so that is a click, not an afternoon.
• Old tutorials. Many examples online use patterns that are deprecated. We build from current documentation.
• Hidden prompts. Some prebuilt chains carry their own prompt text. We replace those with prompts you can read and version.
• Memory defaults. Keeping the whole chat history in every call inflates cost and slows replies. We summarize or window it.

Rule we apply: if a LangChain helper hides something you would want to log, we write that part ourselves.

How we structure the code so you can leave

Your business logic should not depend on framework classes. We keep prompts in plain files, tools as ordinary functions with type hints, and the framework only at the edges where it wires pieces together. If you later drop LangChain, you replace the wiring and keep the rest.

You get a README that lists every prompt, tool and model setting, the evaluation set, and a note on which dependencies are pinned and why. That is the handover standard we aim for on all builds.

Timeline

Planning estimates for a first production release. The fixed quote replaces them with dates after we have seen your data.

Agents with write access take longer than read-only assistants because the approval flow needs its own testing.

Choosing a model and a hosting setup

LangChain does not lock you to one vendor. We usually test two models on your test set before choosing, since the cheaper one often passes for simple routing tasks and the larger one is needed only for the final answer. See OpenAI API vs Claude API for how we compare them. Hosting follows your data rules: your cloud account, or ours with a clear handover.