AI & Automation

LangChain Development Services

LangGraph when an agent needs a real state machine, LangSmith to inspect what it did, and a plain function call wherever the framework adds nothing.

Get a free estimate Speak with an engineer

NDA on request · Free, no-obligation estimate · Reply within one business day

NDA signed before we start

Full IP ownership on payment

Your repository from the first commit

Live demo at the end of every sprint

Built by senior engineers

How we work

LangChain's value is orchestration: coordinating several model calls, tool invocations and conditional branches into a workflow that must hold up over many runs and beyond the one you tested by hand. LangGraph, built on LangChain, models that workflow as an explicit state graph, with nodes for each step and edges for the transitions between them. That makes a multi-step agent's control flow readable and debuggable instead of buried in nested conditionals.

LangSmith is the part of the ecosystem that pays for itself once an agent is running in production: every run gets traced, so a wrong tool call or a malformed output shows exactly which step produced it, and evaluation datasets let a prompt or graph change get checked against known cases before it ships. We use LangSmith on any agent complex enough that debugging by reading application logs stops being practical.

We are also direct about when LangChain is the wrong choice. A workflow that calls one model once, formats the output and returns it does not need a graph framework, an orchestration library or a state machine; a plain function calling the model API directly is less code, fewer dependencies to keep current, and easier for the next engineer to read. We reach for LangChain and LangGraph when the orchestration itself has real complexity to manage, not as a default starting point for every AI feature.

Fit

When LangChain is the right choice, and when it is not

Good fit

The workflow involves multiple steps with real branching: an agent that decides which tool to call next based on a previous result, retries a step differently on failure, or loops until a condition is met
The system needs durable, inspectable state across a multi-turn interaction, where LangGraph's explicit graph structure keeps the control flow legible as more steps get added over time
You need production observability into what an agent actually did on a given run, which tool it called, what it returned, where it deviated from the expected path, that LangSmith's tracing provides out of the box
The team is building several agent-style features and wants a consistent orchestration pattern across them rather than a different bespoke structure for each one

Consider something else

The task is a single model call with a fixed prompt and a parsed response; a direct API call handles this with less code and fewer moving parts than routing it through an orchestration framework built for multi-step workflows
The team has one or two straightforward integrations and no plan to build more agent-style features soon; the framework's abstractions cost more to learn and maintain than they save on a single simple use case
Debugging needs to stay close to plain application code and logs; a framework's abstraction layer can make it harder to see exactly what request is being sent to the model, which matters when a team is still learning how the underlying API behaves
The orchestration logic is genuinely simple enough that a small, well-tested custom function outperforms a general-purpose framework on readability, even if the framework could technically express the same logic

What we build

What we build with LangChain

Agent and workflow orchestration
Multi-step workflows built in LangChain, coordinating model calls, tool invocations and conditional logic into a structure that stays legible as more steps are added.
LangGraph state machines
Agent control flow modeled as an explicit graph of nodes and edges, making branching, retries and loops visible in the graph structure rather than buried in nested application code.
LangSmith tracing and evals
Full run tracing for production agents, showing exactly which tool was called and what it returned on a given run, plus evaluation datasets that check a prompt or graph change against known cases before it ships.
Tool and retrieval integration
Custom tools connecting an agent to your own APIs and databases, plus retrieval components for pulling relevant context from a vector store into the model's input.
Thin custom orchestration
Direct model API calls without a framework where the workflow is simple enough that LangChain's abstractions would add code and dependencies without adding clarity.

Ecosystem

Libraries and tools we pair it with

LangGraph for stateful agent workflows
LangSmith for tracing and evaluation
Vector stores (pgvector, Pinecone, Qdrant) for retrieval
OpenAI and other model provider APIs as the underlying model layer
Python or TypeScript as the host language

LangGraph for agent and state-machine orchestration

A multi-step agent's control flow, which tool to call next, when to retry, when to stop, gets hard to follow once it is expressed as nested conditionals inside a single function. LangGraph models that same logic as a graph: each step is a node, each possible transition is an edge, and the current state moves through the graph explicitly rather than implicitly through call stack depth.

That structure matters most once an agent has enough steps that reading the code top to bottom no longer tells you what it actually does at runtime. For a workflow with two or three fixed steps, the graph abstraction adds structure the workflow does not need yet; for one with real branching and retry logic, it is usually the difference between a workflow a team can extend confidently and one nobody wants to touch.

LangSmith for tracing and evals

Once an agent handles real traffic, the question stops being whether it works on the cases you tested and becomes what it actually did on the case that just failed. LangSmith traces every run through the graph, recording each model call, tool invocation and intermediate result, so a bad outcome can be traced back to the specific step that produced it instead of guessed at from an error message.

Evaluation datasets built in LangSmith let a prompt or graph change run against a fixed set of known cases before it reaches production, which catches the regressions that manual testing on a handful of examples tends to miss. We treat this as part of shipping any agent complex enough to have failure modes worth tracking, not as an optional add-on after launch.

Every run is traced: which tool was called, what it returned, where the agent deviated
Evaluation datasets catch regressions before a prompt or graph change ships
Tracing matters more as agent complexity grows, not from day one on a simple workflow

When thin custom orchestration beats the framework

A workflow that sends one prompt, gets one response and parses it does not benefit from a graph framework, and building it through LangChain anyway adds a dependency, a learning curve for the next engineer, and an abstraction layer between the code and the actual API request being sent. We write that case as a direct function call against the model API, which is shorter, has fewer moving parts to keep updated, and is easier to debug when something about the raw request or response needs inspecting.

The honest test is whether the orchestration itself has complexity worth managing: branching logic, retries, multiple tools, state that persists across turns. When it does, LangGraph earns its cost. When it does not, a framework is overhead without a corresponding benefit, and we say so rather than routing every AI feature through the same tooling by default.

Services

Services that use LangChain

Industries

Where we use it

FAQ

Questions about LangChain

Do we need LangChain for a simple AI feature, or is a direct API call enough?

For a single model call with a fixed prompt and a parsed response, a direct API call is usually simpler and easier to maintain than routing it through an orchestration framework. LangChain and LangGraph earn their cost once the workflow has real branching, retries or multiple tools to coordinate, not for a single-step integration.

What is the difference between LangChain and LangGraph?

LangChain provides the building blocks, model wrappers, tool interfaces, retrieval components, that an application assembles into a workflow. LangGraph, built on top of LangChain, models a multi-step agent's control flow as an explicit graph of nodes and edges, which keeps branching and retry logic visible and debuggable as a workflow grows more complex.

How does LangSmith help once an agent is in production?

It traces every run through the workflow, recording each model call and tool invocation, so a wrong output can be traced back to the specific step that caused it instead of guessed at from an error message. It also runs evaluation datasets against a prompt or graph change before it ships, catching regressions that manual testing on a few examples tends to miss.

Can LangChain call our own internal APIs and databases, not just external tools?

Yes. Custom tools are how an agent built in LangChain reaches your own systems, an internal API, a database query, a search index, and the tool definitions describe exactly what the agent can and cannot do, which keeps its actions scoped to what you have explicitly exposed to it.

Is LangChain tied to a specific model provider?

No. LangChain wraps multiple model providers behind a common interface, so a workflow built against one provider's model can generally switch to another with a configuration change rather than a rewrite, though provider-specific features like certain tool-calling formats may need adjustment.

Compare

Alternatives we also work with

OpenAI & LLM APIsWe build directly against the OpenAI API, covering tool calling and structured outputs as well as the data controls and evals a production system needs.

Insights

Guides on this topic

10 min readHow to Build an AI Application for ProductionHow to build an AI application: choose an approach, prepare data, test model output, add guardrails, manage cost and privacy, and launch with a narrow scope.

Talk to a LangChain developer

Send a short description of the project. A senior engineer reads each request and answers with questions, next steps and an early budget range.