Technology · LangChain

LangChain development services

LangChain and LangGraph are good at wiring models, retrievers and tools together, and easy to turn into an unmaintainable tangle. We build with them when they help, keep the chains small and traceable, and use plain code where a framework adds nothing.

  • Senior engineers on every project
  • Evaluation before launch, not after
  • You own the code and the models
48-hour turnaround · free · no obligation

Talk to us about LangChain

Tell us what you want to build. A senior engineer sends back a scope and a fixed price within two business days, no obligation.

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5.0 on Clutch·200+ projects·Production AI in 3 to 8 weeks

What we build

What we build with LangChain

RAG pipelines

Loaders, splitters, retrievers and reranking set up for your documents, with answers that cite their sources.

LangGraph agents

Multi-step workflows that can branch, retry, wait for a human and resume, with state stored properly so a crash does not lose work.

Tracing and evaluation

LangSmith or open-source tracing so you can see exactly which step failed and why.

Rescue projects

Taking over a prototype that grew too fast, adding tests, and simplifying it until the team can change it safely.

Fit

Is LangChain the right choice?

When it is a good fit

  • The workflow has several steps, tools or branches.
  • You want to swap models or vector stores without a rewrite.
  • You need tracing to debug what an agent actually did.

When we would suggest something else

  • A single prompt and one API call. LangChain is overkill there.
  • Your team does not want another framework dependency. Plain SDK code is a fine choice.

Process

How a project usually runs

  1. 01

    Review or design

    For existing code we start with a short review. For new builds we sketch the graph before writing it.

  2. 02

    Build the smallest working path

    One end-to-end path first, traced and tested, before adding branches.

  3. 03

    Evaluate

    A test set run on every change, with failures traced back to the step that caused them.

  4. 04

    Hand over

    Clear structure, documentation and patterns your developers can extend.

Pitfalls

What usually goes wrong

We have seen these enough times to plan around them from the start.

  • Deep chains nobody can debug. Keep steps small and named.
  • Upgrading LangChain versions without tests. The API moves quickly.
  • Using an agent where a fixed workflow would do. Agents are harder to predict.

Next step

Want to see similar work? Browse our case studies or tell us what you are working on.

FAQ

LangChain: common questions

LangChain or LangGraph?

LangChain for building blocks such as loaders, retrievers and model wrappers. LangGraph when you need a stateful, multi-step flow with branching, retries or human approval.

Is LangChain production ready?

It can be, with discipline: tracing, tests, pinned versions and small, readable chains. Most production problems we see come from how it was used, not from the library.

Can you fix our existing LangChain app?

Yes. We usually start with a review, then stabilise the parts that fail most and add an evaluation set so improvements can be measured.

Do you also use LlamaIndex?

Yes. For document-heavy retrieval LlamaIndex is sometimes the cleaner choice, and the two can work together.

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