How to Use LangChain for Production

LangChain in production: what to use what to avoid and how to keep it maintainable.

LangChain is the most widely used LLM application framework and one of the most frequently cited sources of maintenance pain in production AI systems. This guide covers when LangChain is the right choice which components to use which to replace with custom code and how to structure LangChain applications that stay maintainable as requirements evolve.

No fluff. Production-grade answers from engineers who ship AI into real products.

When to Use LangChain (And When to Write Custom Code)

LangChain adds value when: you want document loaders text splitters and vector store integrations out of the box a structured way to chain LLM calls built-in streaming support and LangSmith observability with minimal setup. LangChain adds friction when: you need precise control over prompt construction need to optimize for token efficiency are building a simple single-LLM-call feature or need fast iteration without API surface changes blocking you. For simple integrations the OpenAI SDK directly is less code and easier to debug.

At Valletta Software, we focus on:

LCEL (LangChain Expression Language): use for chaining - cleaner than legacy chain classes

Document loaders: use built-in loaders for PDFs Word Excel web - no need to build these

Text splitters: RecursiveCharacterTextSplitter with custom separators - sensible default

Vector store integrations: use LangChain wrappers for Pinecone Qdrant pgvector - saves boilerplate

LangSmith: trace every LLM call in development - essential for debugging and optimization

Custom LLM calls: bypass LangChain for simple single-call features - less abstraction overhead

Memory: ConversationBufferWindowMemory for chat - custom for complex multi-turn state

The LangSmith Setup That Makes LLM Apps Debuggable

LLM apps without tracing are debugging in the dark. LangSmith is the solution.

We give you more than just people. We give you top performers who drive results.

Environment variables: LANGCHAIN_TRACING_V2=true LANGCHAIN_API_KEY - one line enables tracing

Trace every run: see exact prompts token counts latency and outputs - not just final results

Evaluation datasets: create golden datasets in LangSmith - run evals on every code change

Prompt hub: version and share prompts in LangSmith Prompt Hub - no more hardcoded strings

Error debugging: full trace shows exactly which step failed and why - no more print debugging

Cost tracking: tokens per run per component - identify expensive chains before they scale

AB testing: compare prompt versions on same dataset - data-driven prompt improvement

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How to Use LangChain for Production - With Engineers Who Know When Not To

Forget the hype. We make AI work in the real world.

Our engineers are trained in the latest AI tooling - Copilot, Claude Code, Cursor, LangChain, and vector databases - and use them daily to ship production AI features, not just prototypes.

Choose from a solo dev, mini team, or full squad. All powered by AI and ready to build from day one.

Lets keep it simple.

Our AI engineers use LCEL for chain composition LangSmith for tracing and evaluation LangChain document loaders for ingestion pipelines and custom OpenAI SDK calls for simple features. We choose per feature not per religion.

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