Which models does it work with?

All the major providers and most open models, through a common interface. That is a large part of its value, since it keeps your application from being tied to one supplier’s API and makes evaluating alternatives a realistic exercise.

How do you keep versions stable?

Dependencies are pinned and upgrades are deliberate rather than automatic, which matters here because the ecosystem moves quickly. Builds are reproducible from a lockfile, so an environment that worked last quarter still works when someone rebuilds it.

Can we see what the system is doing?

Yes, and we would not build one of these without it. Every retrieval, prompt, and tool call is traced, so when an answer is wrong you can follow the chain and find where it went wrong rather than adjusting prompts and hoping for improvement.

Who maintains LangChain?

It is an open-source project with a company of the same name behind it, offering the hosted tracing and evaluation tooling commercially. The core framework is open, so a change in that company’s direction does not strand your application.

LangChain
LLM Development

Stateside Digitals builds LangChain LLM applications: retrieval over your own documents, agents that carry out several steps, and tracing that shows exactly what happened when something returns the wrong answer.

LangChain is a framework for building applications on top of language models. It provides the pieces that sit between a model and your software: connections to different providers, document loading and splitting, retrieval from vector stores, conversation memory, and orchestration for work that takes several steps rather than a single exchange.

None of that is impossible to write yourself, and plenty of capable teams do exactly that. What the framework offers is that the pieces already interoperate, so changing model provider or vector database becomes configuration rather than a refactor. Whether that trade is worth the added abstraction depends entirely on how complicated your application is.

One Interface, Many Models

Swap Providers Without Rewriting

Application logic stays separate from whichever model sits behind it, so moving between providers is a settings change rather than a project.

Traceable By Default

See Every Step It Took

Each retrieval, prompt, and tool call is recorded, so a bad answer can be traced to its cause instead of being guessed at afterward.

Agents That Hold State

Multi-Step Work, Not One Reply

Tasks that need several decisions in sequence run as a managed graph, with defined stopping points rather than an open-ended loop.

Retrieval Pipelines

Chunking, embedding, and ranking your documents so the model receives the right passages rather than whatever matched a keyword.

Multi-Step Agents

Processes that gather information, decide what to do next, and act across several systems, with defined boundaries so they finish rather than running until somebody stops them.

Tool And API Access

Giving a model controlled access to your internal services, so it can look up an order or check availability rather than inventing one.

It is a fair question and the skepticism was earned. Early versions wrapped straightforward API calls in layers that made debugging harder than writing the call directly would have been, and a great many experienced teams concluded they were better off without it. That criticism travelled widely and still shapes how the project is regarded today.

The framework has changed considerably since then, with agent orchestration and tracing now handled by dedicated components rather than improvised on top. Our position is that it earns its place on applications involving retrieval, several steps, and multiple tools, and does not earn it on a single prompt and a reply. We use it where complexity justifies it and go direct where it does not.

We work in Python with LangChain for retrieval and provider abstraction, LangGraph where a task needs state across several steps, and LangSmith for tracing and evaluation. Embeddings are stored in pgvector, Qdrant, or a managed equivalent. Provider adapters cover OpenAI, Anthropic, Google, and open models, so the same application can move between them.

LangChain
LangGraph
LangSmith
Python
pgvector
Qdrant
OpenAI
Anthropic
Google
Open Models
Retrieval
Tracing

Framework Where It Helps

Direct SDK Where It Doesn’t

Every Run Traced

Pinned And Reproducible

Retrieval, several tools, and multi-step work are where the abstraction pays for itself, and that is where we reach for it.

A single prompt and a reply is simpler and easier to maintain written against the provider directly, so that is how we write it.

Retrievals, prompts and tool calls are recorded from the first build, because a wrong answer you cannot follow is one you can only guess at.

Dependencies are pinned and builds come from a lockfile, which matters more here than most places because the ecosystem moves quickly.

Framework Where It Helps

Direct SDK Where It Doesn’t

Every Run Traced

Pinned And Reproducible

Frequently not. If your feature is a prompt, a response, and nothing else, calling the provider directly is simpler and easier to maintain. LangChain earns its place once retrieval, several tools, or multi-step reasoning enter the picture and coordination matters.

LangChain
LLM Development