The LangChain agency.Agents that reach prod.
LangChain can build serious LLM apps, but a notebook that works once and an agent that survives production are very different things. We design the agent and RAG architecture, build stateful agents on LangGraph, and instrument it with LangSmith tracing and evals you can trust.
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GeminiA LangChain agency ships it to production, not just to a demo.
Anyone can wire a chain in a notebook. Designing the right architecture, building stateful agents that don't break, and instrumenting them so you trust them in prod is a different job. Here are the four things we own.
- Architecture
The right agent and RAG architecture, before any code
Most LLM projects fail at the design, not the prompt. Before we write a line, we map what you're actually building: when a stateful LangGraph agent earns its place, when a plain model call is enough, and where retrieval (RAG) belongs. We pick the architecture that fits the job, so you don't over-engineer a single-prompt feature or under-build an agent that needs real control flow.
See a typical design - Build with LangGraph
Stateful, multi-step agents with real control flow
An agent that loses its state on step two isn't production. We build stateful, multi-step agents on LangGraph with the control flow they need: tools, branching, retries, human-in-the-loop checkpoints and guardrails. Each step is scoped and observable, so the agent does the work end to end and you can see exactly what it did and why.
See the method - Observability
LangSmith tracing and evals so you can trust it in prod
You can't debug what you can't see. We wire LangSmith from day one: traces of every step, evals that catch regressions before they ship, and monitoring so you know when an agent drifts in production. That's the difference between an LLM app you babysit and one you trust. We instrument it so your team can debug, measure and improve it without guessing.
See the integrations - Integration & ops
Wired to your data, your stack and your models
LangChain is model-agnostic, and so are we. We route to Claude, OpenAI or open models based on the task, wire the agent to your data and your stack, and deploy and monitor it where it actually runs. We're an automation and AI agency first, so this plugs into how your team already ships instead of becoming a science project nobody owns.
See AI enablement
We build LangChain apps like production software, not a demo.
Most LLM projects die the same way: a chain that works on the happy path, no state, no observability, and it falls apart the moment a real user hits it. So we treat it like software: the architecture designed first, the agent built stateful on LangGraph, the whole thing instrumented with LangSmith so you can debug, measure and trust it once it's live.
- Audit · map the use case and decide where LangChain actually helps, and where it's over-engineering
- Design · the agent, RAG and state architecture, before any code goes in
- Build · stateful agents on LangGraph with control flow, tools and guardrails
- Instrument · LangSmith tracing and evals so you can trust and debug it in production
We build agents that survive production.
We don't sell a partner tier. We come from automation and AI, so we build LLM agents the way they actually have to run: stateful on LangGraph, grounded with RAG, instrumented with LangSmith, and honest about when a framework is over-engineering for your use case. That's exactly what's missing when a project ends at a notebook that worked once.
- We come from automation and AI, so we build agents that survive production, with state, evals and observability, not a demo that breaks on the second message.
- Observability by default: LangSmith tracing and evals are wired from day one, so you debug and trust the app instead of babysitting it.
- We're honest about when a framework is over-engineering. If a plain model call beats a LangGraph agent for your use case, we'll tell you.
- No partner badge to sell. We're judged on whether your LLM app actually reaches production and stays reliable, not on a tier.
LangChain at the core, your data and stack around it.
We build the parts that turn an LLM 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 architecture before the code: when a LangGraph agent earns its place, when a plain call is enough, where retrieval belongs, and how state and tools should flow, so you build the right thing once.
- Build
LangGraph stateful agents
We build multi-step agents on LangGraph with proper control flow: tools, branching, retries, checkpoints and human-in-the-loop gates, each step scoped so the agent owns the task without going off the rails.
- Build
LangSmith tracing & evals
We instrument the app with LangSmith from day one: traces of every step, evals that catch regressions, and monitoring so you can debug, measure and trust the app once it's in production.
- Build
RAG & retrieval
We build the retrieval layer that grounds your agent in your data: chunking, embeddings, vector store, and the retrieval logic that actually returns the right context, evaluated so answers stay accurate.
- Build
Model-agnostic routing
LangChain works with Claude, OpenAI and open models. We route each task to the model that fits on cost, speed and quality, so you're never locked to one provider or paying premium rates for simple calls.
- Build
Deployment & ops
We deploy the app to your stack, wire it to your data and APIs, and set up monitoring and logging so it runs reliably, with the observability your team needs to keep it healthy after we leave.
We pressure-test your LLM 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 simpler approach beats it. Zero pitch, just an engineer's take on your use case.
- An honest read on whether LangChain fits your use case
- The agent, RAG and state architecture to build
- The model strategy worth using
- A frank take on when 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're 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 doesn't. For a complex, stateful, team-scale agent it's a real accelerator. For a single-prompt feature, a framework is often over-engineering, and we'll say so before you commit to it.
- Step 2 · Architecture design
Design the agent, RAG and state before any code
We map the architecture first: where a LangGraph agent with state and control flow belongs, where a plain model call is enough, and where retrieval (RAG) should ground the answers. We pick the model strategy too, since LangChain is model-agnostic. An engineer on your side signs off on the design before we build, so you're not paying to discover the architecture mid-project.
- Step 3 · Build with LangGraph
Stateful agents with real control flow
We build the agent on LangGraph: stateful, multi-step, with the tools, branching, retries and human-in-the-loop checkpoints the job needs. Each step is scoped and has only the tools it should, with guardrails so the agent can't run wild. The retrieval layer is built and evaluated so answers stay grounded. You get an agent that owns the task end to end, not a brittle prompt chain.
- Step 4 · Instrument & integrate
Wire LangSmith, your data and your stack
We instrument the app with LangSmith: traces of every step, evals that catch regressions, and monitoring for production drift. Then we wire it to your data, your APIs and your stack, with model-agnostic routing to Claude, OpenAI or open models per task. Everything ships with its observability and 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 instrumented and documented, so your team can read the traces, run the evals and improve it without us. If you want to go deeper, our AI training covers LangChain, LangGraph and LangSmith end to end. If you want us on call for what scales next, we talk about that separately, no lock-in.
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 production LLM apps and agents with the framework, instead of leaving you with a notebook that works once. We design the agent and RAG architecture, build stateful agents on LangGraph with proper control flow and guardrails, wire LangSmith for tracing and evals, and connect it to your data, stack and models. The point is an LLM app that reaches production and stays reliable, not a demo that breaks on the second message.How much does a LangChain project cost?
It depends on scope: a single retrieval-backed agent is nothing like a multi-agent system wired into your data and your stack with full observability. We don't throw out a flat package. We start with a free 60-minute audit to find where LangChain actually helps your use case, then quote a fixed scope. Model usage you pay the provider directly (Claude, OpenAI or open models); we design the routing so the bill stays predictable.When should we NOT use LangChain?
When it's 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 is simpler, cheaper and easier to maintain than a framework. LangChain earns its place when you have complex, stateful, multi-step agents and a team that has to maintain them over time. We'll tell you honestly which side of that line you're on before you commit, because the wrong tool slows you down.What is LangGraph and when do we need it?
LangGraph is LangChain's library for building stateful, multi-step agents and graphs: it gives you control flow, branching, retries, checkpoints and human-in-the-loop gates that a plain prompt chain doesn't. You need it when the agent has to hold state across steps, call tools, make decisions and recover from failures, in other words a real agent rather than a single answer. For a one-shot prompt you don't need it, and we won't add it just to look sophisticated.How do you make a LangChain app reliable in production?
With observability and evals, wired from day one. We instrument the app with LangSmith so every step is traced, set up evals that catch regressions before they ship, and add monitoring so you know when an agent drifts in production. That's the difference between an LLM app you babysit and one you trust. We also build guardrails and human-in-the-loop checkpoints into the LangGraph flow so the agent can't go off the rails unseen.Is LangChain locked to one model provider?
No, LangChain is model-agnostic and works with Claude, OpenAI and open models. We use that on purpose: we route each task to the model that fits on cost, speed and quality, so you're never locked to one provider or paying premium rates for simple calls. If your needs change or a better model ships, swapping is a config change, not a rebuild. We design the routing layer so you keep that flexibility.What's the difference between LangChain, LangGraph and LangSmith?
They're three parts of the same stack. LangChain is the framework for building LLM applications. LangGraph is its library for stateful, multi-step agents and graphs, the part you reach for when you need real control flow. LangSmith is the observability platform: tracing, 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.Can you build RAG with LangChain?
Yes, retrieval is one of the things the framework is built for. We build the retrieval layer that grounds your agent in your data: chunking, embeddings, a vector store, and the retrieval logic that actually returns the right context. Then we evaluate it with LangSmith so answers stay accurate instead of confidently wrong. RAG is only as good as its retrieval, so we measure it rather than assume it works.
Stop shipping demos. Ship an agent that lasts.
A 60-minute audit, your LLM use case pressure-tested, an architecture plan with the observability 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.