The LangChain agency.Custom AI that reaches production.
LangChain is the developer toolkit for building custom AI applications: assistants that answer in plain language, document search over your own files, and agents (software that decides its next step and acts, not just replies). When a visual automation tool like n8n hits its limit, we write the custom code on top: we design the architecture, build the agents, and wire in the tracing and tests so you can trust the thing in production.
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GeminiA LangChain agency ships it to production, not just to a demo.
Anyone can get a demo working in a notebook once. Designing the right architecture, building custom AI that holds up, and instrumenting it so you trust it in production is a different job. Here are the four things we own.
- Architecture
The right design, before any code
Most AI projects fail in the design, not the prompt. Before writing a line, we map what you are actually building: when a real agent earns its place, when a single model call does the job, and where the app needs to pull answers from your own documents (that retrieval step is called RAG). You get the right thing built once, instead of an over-engineered feature or an agent that cannot hold its state.
See a typical design - Build the agents
Stateful, multi-step agents that do the whole job
An agent that forgets what it was doing on step two is not production. We build agents that keep their state across steps, call your tools, branch, retry and pause for a human when they should. Each step is scoped and visible, so the agent runs the task end to end and you can see exactly what it did and why. LangGraph is the part of the toolkit that makes this control flow reliable.
See the method - Tracing & tests
You can see everything, so you can trust it in prod
You cannot debug what you cannot see. We wire in tracing from day one: a full record of every step, automated tests (evals) that catch problems before they ship, and monitoring that flags when the AI starts drifting in production. That is the line between an app you babysit and one your team actually trusts. LangSmith is what we use to make every step visible.
See the method - Integration & ops
Wired to your data, your stack and your models
LangChain works with any model, and so do we. We route each task to Claude, OpenAI or an open model based on cost, speed and quality, connect the app to your data and your tools, then deploy and monitor it where it runs. We are an automation and AI agency first, so this plugs into how your team already ships instead of turning into a project nobody owns.
See AI enablement
We build LangChain apps like production software, not a demo.
Most custom AI projects die the same way: a demo that works on the happy path, no memory of what it just did, no way to see inside it, and it falls apart the moment a real user shows up. So we treat it like software: the architecture designed first, the agent built to hold its state, the whole thing traced and tested so you can debug, measure and trust it once it's live.
- Audit. We map the use case and say plainly where LangChain helps, and where it would be over-engineering.
- Design. We settle the agent, retrieval (RAG) and state architecture before a line of code goes in.
- Build. Stateful agents with real control flow, your tools, and guardrails so nothing runs wild.
- Instrument. Tracing and automated tests wired in, so you can trust and debug the app in production.
n8n or LangChain: when visual automation is enough, and when you need code.
We do both, so we can be straight with you. n8n and other visual tools cover a huge amount: connect two apps, move data, trigger a workflow, even call a model in a box. But some jobs need real code. An agent that holds its state across many steps, retrieval tuned over your own documents, custom logic no drag-and-drop tool exposes: that is where LangChain earns its place. The honest rule we work by: start with the visual tool, reach for custom code only when it stops being enough.
- n8n and visual tools win when the job is connecting apps and moving data on a trigger. Fast to build, easy to maintain, and often all you need.
- LangChain wins when you need a stateful agent, tuned retrieval over your own documents, or logic a visual tool cannot express. That is custom software, and we write it.
- We build both, so the recommendation stays honest: if n8n does the job, we say so and save you the cost of a custom build.
- No partner badge to sell. We are judged on whether your AI app reaches production and stays reliable, not on a vendor tier.
LangChain at the core, your data and stack around it.
We build the parts that turn an AI idea into a reliable production app, then connect them to your data and the way your team ships. Here's what a real LangChain build covers.
- Build
Agent & RAG architecture
We design the whole system before the code: when an agent earns its place, when a plain model call is enough, and where retrieval over your own documents (RAG) belongs, so you build the right thing once instead of discovering it mid-project.
- Build
Stateful agents
We build multi-step agents that keep their state, call your tools, branch, retry and pause for a human when needed. Each step is scoped so the agent owns the task without going off the rails.
- Build
Tracing & tests
We instrument the app from day one: a trace of every step, automated tests that catch regressions, and monitoring so you can debug, measure and trust the app once it is live in production.
- Build
RAG over your data
We build the retrieval layer that grounds the AI in your own files: chunking, embeddings, a vector database and the logic that returns the right passage, measured so answers stay accurate instead of confidently wrong.
- Build
Model-agnostic routing
LangChain runs on Claude, OpenAI or open models. We route each task to the one that fits on cost, speed and quality, so you are never locked to a single provider or overpaying for simple calls.
- Build
Deploy, monitor, hand over
We ship the app to your stack, wire it to your data and APIs, and set up the logging and alerts your team needs to keep it healthy and improve it long after we step away.
We pressure-test your AI use case, you leave with a plan.
Before quoting anything, we take 60 minutes to look at what you're building, your data and the team that has to maintain it. You leave with an honest read on whether LangChain fits, what architecture to build, and whether a visual tool like n8n beats it. Zero pitch, just an engineer's take on your use case.
- An honest read on whether LangChain fits, or whether n8n does the job
- The agent, RAG and state architecture worth building
- A ballpark on cost (from $2,000) and a realistic timeline
- The model strategy, and where a simpler approach wins
How we run a LangChain build.
Five steps, in order. We don't write code before the architecture is signed off, we don't ship an agent without observability, and your team owns it at the end. Each step has a deliverable and you sign off before we move on.
- Step 1 · Use-case audit
Decide where LangChain actually helps
We sit down and look at what you are really building: the use case, the data, the volume, the team that has to maintain it. Half the value is telling you where LangChain earns its place and where it does not. For a complex, stateful agent it is a real accelerator. For a single-prompt feature, a visual tool like n8n is usually simpler, and we will say so before you spend a euro.
- Step 2 · Architecture and quote
Design it, then put a real number on it
We map the architecture first: where a stateful agent belongs, where a plain model call is enough, and where retrieval over your documents (RAG) should ground the answers. We pick the model strategy too, since LangChain runs on any provider. Then you get a fixed quote, from $2,000 and up depending on scope, with a timeline that runs anywhere from one week to six months. An engineer on your side signs off before we build, so nothing is billed until the plan and the price are agreed.
- Step 3 · Build the agent
Stateful agents with real control flow
We build the agent: stateful, multi-step, with the tools, branching, retries and human checkpoints the job needs. Each step has only the tools it should, with guardrails so it cannot run wild. The retrieval layer is built and measured so answers stay grounded in your data. You get an agent that owns the task end to end, not a brittle prompt chain that breaks on the second message.
- Step 4 · Instrument & integrate
Wire the tracing, your data and your stack
We wire in the tracing and automated tests: a record of every step, evals that catch regressions, and monitoring for drift in production. Then we connect it to your data, your APIs and your stack, routing each task to Claude, OpenAI or an open model. Everything ships with its logging from day one, so the app is debuggable the moment it goes live.
- Step 5 · Deploy & hand over
Ship it, then make it yours to run
We deploy the app to your stack and hand it over documented and instrumented, so your team can read the traces, run the tests and improve it without us. Want your people fully autonomous? Our AI training covers LangChain, LangGraph and LangSmith end to end. Want us maintaining it and shipping the next phase? We cover that separately, with no lock-in. Either way, the code and the app are yours from day one.
We're judged on the apps that ship.
No partner badge to display, so we lead with what matters: feedback from the teams whose LangChain apps we built, and whether those apps actually reached production and stayed reliable after we left. Our Trustpilot reviews come from those teams, not from a marketing deck.
- The app lives in your repo and stack, owned by your team
- LangSmith tracing and evals wired before anything goes live
- Agents stateful, grounded with RAG, and kept observable
- Trustpilot reviews come from the teams we built for
The questions we get asked on repeat.
What does a LangChain agency actually do?
A LangChain agency builds custom AI applications with the framework, instead of leaving you with a notebook that ran once. We design the agent and retrieval (RAG) architecture, build stateful agents that hold state and call your tools, wire in tracing and automated tests, then connect it to your data, stack and models. The goal is an AI app that reaches production and stays reliable, not a demo that breaks on the second message. Most projects that stall never got past that first working notebook.What is LangChain, in plain terms?
LangChain is a developer toolkit for building custom AI applications on top of language models. Think assistants that answer in plain language, document search over your own files, and agents that take real actions across several steps rather than replying once. It is not a no-code product you click together, it is a framework developers build with. That is why teams hire an agency for it: the value is in designing and coding the app well, not in the library itself.n8n or LangChain: which one do we need?
Start with n8n or another visual automation tool. If the job is connecting apps, moving data and triggering workflows, a visual tool is faster to build and easier to maintain, and it can call a model too. You reach for LangChain when you need custom code a drag-and-drop tool cannot express: an agent that holds state across many steps, retrieval tuned over your own documents, or logic no node exposes. We build both, so the honest answer is usually simple: use the visual tool until it stops being enough, then write the custom AI.When should we NOT use LangChain?
When it is over-engineering for the job. If your feature is a single prompt with no state, no tools and no multi-step flow, a plain model call or an n8n workflow is simpler, cheaper and easier to maintain. LangChain earns its place with complex, stateful, multi-step agents that a team has to maintain over time. We will tell you honestly which side of that line you are on before you commit, because the wrong tool slows everyone down.What are the alternatives to LangChain?
Plenty, and the right one depends on the job. For simple app-to-app automation with a bit of AI, n8n, Make or Zapier are often enough. For custom agents, LlamaIndex leans into retrieval, and you can also build straight on a provider SDK from Claude or OpenAI with no framework at all. LangChain's strength is the ecosystem: LangGraph for stateful agents and LangSmith for observability in one place. We are not married to it. We pick per use case and tell you when a lighter option wins.What is the difference between LangChain, LangGraph and LangSmith?
Three parts of the same stack. LangChain is the framework for building AI applications. LangGraph is its library for stateful, multi-step agents, the part you reach for when the AI has to hold state, make decisions and recover from failures. LangSmith is the observability tool: tracing, automated tests (evals) and monitoring so you can debug and trust the app in production. We use all three together: design and build with LangChain and LangGraph, then instrument with LangSmith.How much does a LangChain project cost?
It starts at $2,000 and moves with scope: a single retrieval-backed agent is nothing like a multi-agent system wired into your data with full observability. We skip the flat package. After a free 60-minute audit we scope the work and give you a fixed quote, with a timeline that usually lands between one week and six months. Model usage you pay the provider directly (Claude, OpenAI or an open model), and we design the routing so that bill stays predictable.How do you make a LangChain app reliable in production?
With observability and tests wired in from day one. We trace every step so nothing is a black box, set up automated evaluations that catch regressions before they ship, and add monitoring so you know when the AI drifts. That is the difference between an app you babysit and one you trust. We also build guardrails and human checkpoints into the agent flow, so it cannot take an irreversible action unseen. Reliability is designed in, not bolted on after launch.Can you build RAG over our own data?
Yes, grounding the AI in your data is one of the main things the framework is built for. We build the retrieval layer, called RAG: chunking your documents, turning them into embeddings, storing them in a vector database, and writing the logic that returns the right passage for each question. Then we measure it, because RAG is only as good as its retrieval, so answers stay accurate instead of confidently wrong. Your data stays in your own stack throughout.What happens after you deliver?
The app is yours from day one: the code lives in your repo, in your stack, owned by your team. We hand it over documented and instrumented, so your people can read the traces, run the tests and improve it without us. If you want them fully autonomous, our AI training covers LangChain, LangGraph and LangSmith end to end. If you want us maintaining it and shipping the next phase, we cover that separately, with no lock-in.
Stop shipping demos. Ship an agent that lasts.
A 60-minute audit, your AI use case pressure-tested, an architecture plan with the tracing and tests baked in. If a simpler approach beats LangChain for your case, we'll tell you. If a real agent is the right call, we build it.