AI & Machine Learning
LangChain
LLM applications, RAG and agent workflows built by engineers who know when LangChain helps — and when it just gets in the way.
Overview
LangChain is an open-source framework for building applications on top of large language models. It offers a layer of abstractions over the raw model APIs: prompt templates, memory, chains that thread several model calls together, document loaders and text splitters for ingesting content, integrations with vector stores for retrieval-augmented generation, and a tool and agent system for letting a model call functions and decide what to do next. Around the core sit two things worth naming separately: LangGraph, for building stateful, controllable agent workflows as an explicit graph rather than a black box, and LangSmith, for tracing, evaluating and monitoring what your LLM app is actually doing in production.
We use LangChain selectively, and we will say so plainly. Its real value shows up in two places. The first is complex, multi-step agentic systems — where a request fans out into retrieval, several model calls, tool use, and branching decisions, and you want structure around that rather than a tangle of bespoke glue. The second is its supporting tooling and breadth: LangSmith gives you observability and evaluation for non-deterministic systems that are genuinely hard to debug, and the enormous catalogue of provider integrations lets you swap models and vector stores without rewriting your application. For those jobs, the framework earns its place.
The honest counterpoint — and it is a widely held one among senior engineers — is that LangChain is easy to reach for when you do not need it. For a single prompt, a classification call, or a simple retrieval feature, its early abstractions can be leaky and over-engineered: you end up debugging through layers of the framework to work out what the model was actually sent, when a thin, direct call to the model SDK would have been shorter, clearer and easier to reason about. We treat LangChain as one tool among several, not a default. Where it fits, we use it well; where it does not, we call the model directly and tell you why.
Best for — Complex, multi-step LLM and agent systems — and teams that need real tracing and evaluation — where LangChain’s structure and integration breadth outweigh its overhead, and a senior team knows when to drop it for a direct model call instead.
Why teams choose LangChain
Structure for multi-step agents
When a request becomes retrieval plus several model calls plus tool use plus branching, LangGraph lets you model that as an explicit, inspectable state machine rather than nested callbacks. The control flow becomes something you can read, test and reason about instead of a black box that only behaves in the demo.
Observability for non-deterministic systems
LLM apps fail in ways ordinary software does not — a subtly wrong answer, a drifted prompt, a tool call that never happened. LangSmith traces every step of a run, so you can see the exact prompt, the retrieved context and the model output, reproduce a bad case, and measure quality with evals rather than guessing from vibes.
Provider portability
The framework’s common interfaces over models and vector stores mean you are not married to one provider. Moving from one model to another, or adding a second store, becomes a configuration change at the edges rather than a rewrite through your application — useful when pricing, latency or capability shifts under you.
Why businesses choose LangChain
- You are building a complex agentic system where explicit orchestration and a standard structure pay for themselves, not a single-call feature where they are overhead.
- You need to observe, reproduce and evaluate a non-deterministic LLM app in production, and you value LangSmith’s tracing and evals as a first-class part of the stack.
- You want the freedom to change models or vector stores without rewriting your application, and the integration breadth to support several providers at once.
- You want a team with the judgement to use LangChain where it helps and to drop it — for a direct model SDK call — where it would only add layers, and to tell you honestly which case you are in.
What we build with LangChain
The capabilities this technology is genuinely strong at — and what we most often build with it.
Retrieval-augmented generation (RAG)
Document loaders, text splitters, embeddings and vector-store retrievers let us ground a model in your own content — so answers cite your data rather than the model’s training. We treat retrieval quality, chunking and reranking as the real work here, because a RAG system lives or dies on what it retrieves, not on the framework wiring around it.
LangGraph agent workflows
For anything beyond a single call, we model the flow as a LangGraph state graph: nodes for retrieval, model calls and tools, explicit edges for branching and loops, and persisted state so a run can pause, resume and be inspected. This is what turns an unpredictable agent into a system you can test and control.
Tool and function calling
We give models a bounded set of tools — search, database lookups, API calls, calculations — and let them decide when to use each, with the framework handling the call-and-return loop. The engineering that matters is the guard rails: validating tool inputs and outputs, so the model can act without being trusted blindly.
LangSmith tracing and evaluation
Every run is traced end to end — prompts, retrieved context, tool calls and outputs — so we can see exactly what happened, reproduce failures, and run evals against datasets to measure whether a change made the system better or worse. For non-deterministic software this is the difference between engineering and guessing.
Prompt templates and structured output
Templates keep prompts versioned and consistent, and output parsers coerce model responses into typed, validated structures your application can rely on. We keep the assembled prompt visible and under review, because a template that quietly produces the wrong prompt is worse than no template at all.
Provider-agnostic model and store integrations
LangChain’s common interfaces let us wire in OpenAI, Claude or Gemini and vector stores like Pinecone or pgvector behind a single abstraction, so switching or combining providers is a change at the boundary rather than a rewrite through the codebase.
Use cases
Retrieval-based assistants over your data
Question-answering and support assistants grounded in your documents, policies or knowledge base, where the value is accurate retrieval and cited answers — and where we invest in chunking, reranking and evaluation rather than accepting the naive default.
Multi-step agents with tools
Systems that plan, retrieve, call tools and act across several steps — research assistants, internal operations agents, workflow automations — built as controllable LangGraph graphs so the behaviour is testable rather than emergent and fragile.
Evaluated, observable LLM features
Production LLM features where you need to know quality is holding: LangSmith traces every run and evals guard against regressions, so a prompt or model change is measured before it ships rather than discovered by a customer.
Multi-provider and model-portable systems
Applications that must support more than one model provider, or expect to switch as pricing and capability change, using LangChain’s integration layer to keep the application code independent of any single vendor.
When LangChain is the right choice
- You are building a genuinely agentic or multi-step system — retrieval, several chained model calls, tool use, and branching logic in one flow — and you want structure and a standard vocabulary around it rather than hand-rolled orchestration that only its author understands.
- You need serious observability and evaluation for a non-deterministic system: LangSmith’s tracing, datasets and evals are a real answer to the hard problem of knowing whether an LLM app is getting better or worse, and reproducing a bad output long after it happened.
- You expect to swap models or vector stores, or to support several at once. LangChain’s large integration surface means you can move between providers — OpenAI, Claude, Gemini, Pinecone, pgvector — behind a common interface instead of rewriting your data and inference layers.
- Wrong for: a single-prompt feature or a simple RAG lookup. If the job is one call to a model, or one embedding search followed by one prompt, a thin direct integration with the provider SDK is clearer, shorter, and far easier to debug. Wrapping that in LangChain adds indirection you will pay for every time something breaks.
- Wrong for: teams that want to understand exactly what the model receives on every call and cannot afford surprises. LangChain’s abstractions can obscure the final prompt and the control flow behind them; if that transparency is non-negotiable for you, either use LangSmith to see through the layers or skip the framework and build the calls explicitly.
LangChain: pros and cons
Strengths
- A large, standard catalogue of integrations — models, vector stores, document loaders, tools — so you rarely write plumbing from scratch and can swap providers behind a common interface.
- LangGraph gives you a real model for stateful, controllable agent workflows, turning multi-step orchestration into an explicit graph you can inspect, test and resume rather than opaque glue.
- LangSmith is genuinely strong: tracing, datasets, and evaluation for LLM apps solve the hard problem of observing and improving systems whose output is not deterministic.
- A shared vocabulary and well-trodden patterns for RAG, memory and agents mean a team can move quickly on complex systems without reinventing the architecture each time.
Trade-offs
- Abstraction overhead, and leaky abstractions. For simple tasks the framework adds indirection that obscures more than it helps, and its early high-level helpers often leak the underlying detail anyway — so you carry the cost without the promised simplicity.
- Fast-moving and prone to breaking changes. The API has churned significantly across versions; code written against one release can need rework on the next, and keeping current is an ongoing maintenance cost you should budget for.
- Debugging through the layers is harder than debugging a direct call. When something goes wrong you are often tracing through framework internals to find where a prompt was assembled or a tool was invoked, which slows down diagnosis.
- It can obscure what the model is actually being sent. The convenience of templates and chains means the final prompt is assembled for you — helpful until it is wrong, at which point you need LangSmith or manual inspection to see the truth of what left your application.
Architecture
The first architectural decision is whether LangChain belongs in the design at all. For a single-prompt or simple-retrieval feature we often build a thin, direct integration with the model SDK — it is clearer and cheaper to run — and reserve the framework for systems with genuine multi-step complexity. When it does earn its place, we structure the flow explicitly with LangGraph rather than leaning on high-level agent helpers that hide the control flow: nodes for each step, explicit edges for branching and retries, and persisted state so runs are inspectable and resumable.
We keep the boundaries clean. Retrieval, model calls, tools and business logic are separable pieces, so we can test each, swap a provider without disturbing the rest, and — importantly — see and log the exact prompt sent on every call. The abstraction is allowed to save us plumbing, never to hide what the model receives. Because the framework’s API moves quickly, we pin versions deliberately and isolate LangChain behind our own interfaces where it makes sense, so a breaking upgrade is a contained change rather than a ripple through the whole application.
Performance
For LLM applications the dominant cost is almost never the framework — it is the model calls: latency and tokens. We design to minimise both, streaming tokens to the user so perceived latency is low, running independent retrieval and model calls concurrently rather than in a needless chain, and caching embeddings and, where safe, responses. The framework overhead itself is small next to a model round trip; the wins come from how the calls are orchestrated, not from micro-optimising LangChain.
The honest caveat is that abstractions can hide inefficiency — an agent that makes more model calls than it needs, a chain that re-embeds or re-retrieves unnecessarily, a prompt quietly ballooning in tokens. This is exactly where LangSmith’s tracing pays off: it shows the real call graph, token counts and timings per step, so we optimise against measured behaviour rather than assumptions. We tune retrieval, prune unnecessary calls, and right-size context windows because tokens are money and latency is user experience.
Security
LLM applications carry a distinct threat model, and LangChain’s tool and agent features make it sharper: prompt injection. Any untrusted content — a retrieved document, a user message, a tool result — can carry instructions the model may follow, so we treat all of it as hostile input. Tool inputs and outputs are validated, and model output never reaches a shell, a database query, or a filesystem without validation and least-privilege boundaries. An agent is given the narrowest set of tools and permissions that lets it do its job, and no more.
Beyond that, the usual discipline applies with an LLM edge. API keys and provider credentials stay in the environment, out of code and out of prompts. Data sent to third-party models is governed by what your compliance posture allows, and we are explicit about where content goes — including to LangSmith, which we configure and access-control deliberately since traces contain real prompts and data. We guard against sensitive information leaking into logs or into a model’s context where it does not belong.
Scalability
A LangChain application scales like the service that hosts it — typically a stateless API tier that we can run in multiple instances behind a load balancer — while conversation and agent state lives in a store such as Postgres or Redis rather than in process memory, so any instance can serve any request. LangGraph’s persisted state fits this model: a run can be checkpointed and resumed on a different worker, which matters for long or interruptible agent flows.
The genuine scaling constraints are external: provider rate limits, token throughput and cost. We design for them with concurrency controls, backoff and retries, queuing for heavy or long-running agent jobs, and caching to avoid repeating identical model calls. Vector stores like Pinecone or pgvector are scaled on their own terms as the corpus grows. The point is that scaling an LLM system is mostly about managing model-call throughput and cost, not about the framework — and we plan capacity around the model provider, which is the real bottleneck.
LangChain integrations & ecosystem
The technologies we most often pair with it — each links to how we work with it.
How we work
We start by deciding whether you need LangChain at all. For a simple feature we will often recommend a direct model integration and save you the framework’s overhead; for a genuinely complex agentic or multi-provider system we bring in LangChain, LangGraph and LangSmith where each earns its place. Then we build in thin slices: one real flow — retrieval, a model call, a tool, an eval — traced and measured end to end, rather than a scaffold that demos well and falls over on the second question.
Because LLM systems are non-deterministic, we treat evaluation as part of the build, not an afterthought. We assemble datasets of real cases, wire LangSmith evals into the workflow, and measure whether a prompt or model change actually helps before it ships. The engineers who design the system run it, so we build for the on-call reality of a system that can fail quietly — with tracing on, prompts visible, and the abstractions kept thin enough that we can always see what the model was sent.
The service behind it
Delivered throughAI DevelopmentWhat we build with LangChain
The disciplines this technology most often shows up in — from a first build to taking over and stabilising an existing one.
How we deliver
- 01
Discover
We map the system, the constraints and the business it serves — including the parts nobody documented.
Architecture brief
- 02
Architect
Decisions get made, written down and defended before a line of production code exists.
Decision records
- 03
Build
Short cycles against working software. You see progress in the product, not in a status deck.
Shipping increments
- 04
Operate
Monitoring, incident response and iteration. The system is alive, so the engagement is too.
Runbooks & SLOs
Industries we use LangChain in
Domain knowledge changes what gets built. A few of the sectors we know before the first meeting.
Also in AI & Machine Learning
Why teams choose us for LangChain
Senior engineers only
LLM systems fail in subtle, non-deterministic ways, and LangChain’s abstractions can hide the cause. The people designing your system have built agentic and RAG systems before, know where the framework leaks, and diagnose through the layers — there are no juniors learning the framework on your project.
We operate what we build
We run the LLM systems we ship, so we build for the reality of quiet failures and drifting quality: tracing on, evals in place, token cost watched, prompts visible. You get a system that stays reliable in production, not just impressive in a demo.
Honest about when not to use LangChain
LangChain is genuinely divisive, and we do not pretend otherwise. If your feature is a single prompt or a simple lookup, we will build it with a direct model call and tell you why the framework would only add overhead. We would rather ship you the simpler thing.
Fluent across the LLM stack
LangChain rarely stands alone. We bring the surrounding pieces — Python or Node, FastAPI backends, Postgres and vector stores like Pinecone or pgvector, and the model providers from OpenAI to Claude and Gemini — so the whole system is built by one team rather than stitched together.
Typical timeline
- 01
Discovery and framing
One to two weeks agreeing the flows, data sources and quality bar — and deciding honestly whether LangChain belongs in the design or a direct model integration serves you better.
- 02
First traced slice
Two to three weeks delivering one real flow end to end — retrieval or agent step, model call, and LangSmith tracing — so we prove the approach against real inputs, not a happy-path demo.
- 03
Iterative build with evals
Flow-by-flow delivery in short cycles, each traced and measured, with evaluation datasets growing alongside so quality is guarded as the system expands rather than checked at the end.
- 04
Hardening and handover
Prompt-injection and permission review, token-cost and latency tuning against traced behaviour, version pinning against framework churn, and documentation so your team can own and extend the system.
How pricing works
- Fixed-scope builds for well-defined LLM features — a RAG assistant, an agent workflow, an evaluation harness — quoted once we understand the data, the flows and the quality bar.
- Monthly senior engagement for ongoing LLM product work, where prompts, models and requirements evolve and you want continuity, tracing and evals maintained over time rather than a one-off deliverable.
- Focused audits and rescues for existing LangChain codebases — leaky abstractions, runaway token cost, an agent that is unreliable, or a system stuck on an old framework version — priced by the assessment.
Hire LangChain engineers
Need LangChain capacity on your own team? We embed named senior engineers into your existing team — reporting to your leads, working in your rituals — so you add capacity without a hiring cycle.
Hire LangChain engineersCommon questions
Do we actually need LangChain, or can we just call the model directly?
Often you can, and should. For a single prompt, a classification call, or a simple retrieve-then-answer feature, a thin direct integration with the provider SDK is clearer, shorter and far easier to debug than the same thing wrapped in LangChain. We recommend the framework when there is real multi-step complexity — chained calls, tool use, branching agents — or when you specifically want its integration breadth or LangSmith’s tracing and evals. If those do not apply to your project, we will tell you to skip it.
Why do some senior engineers dislike LangChain?
Because its early high-level abstractions were leaky and over-engineered for simple tasks, and because the API has churned hard across versions. Reach for a convenience helper and you can end up debugging through framework internals to find out what the model was even sent — when a direct call would have been transparent. Those criticisms are fair, and we share them for simple use cases. Where we disagree is the blanket dismissal: for complex agentic systems, and for LangSmith’s observability, the framework earns its place. The skill is knowing which situation you are in.
What are LangGraph and LangSmith, and do we need them?
LangGraph is for building stateful, controllable agent workflows as an explicit graph — nodes, edges, persisted state — instead of an opaque agent loop; it is what we reach for when a flow has real branching, retries or long-running steps. LangSmith is for observability: tracing every step of a run, reproducing failures, and evaluating quality against datasets. You need LangGraph when your agent is complex enough that you must be able to reason about its control flow, and LangSmith whenever quality and debuggability of a non-deterministic system matter — which, in production, is nearly always.
How do you keep up with LangChain’s breaking changes?
We treat the churn as a known cost, not a surprise. We pin versions deliberately rather than tracking the latest release blindly, isolate LangChain behind our own interfaces where it makes sense so an upgrade is a contained change, and lean on tests and LangSmith traces to catch behavioural regressions when we do upgrade. It is one of the honest downsides of the framework, and we budget maintenance for it up front instead of pretending it away.
Can we change model providers later without rewriting everything?
That portability is one of LangChain’s genuine strengths. Its common interfaces over models and vector stores mean switching from, say, OpenAI to Claude or Gemini, or adding a second provider, is largely a change at the boundary rather than a rewrite through your application. The caveat is that models are not perfectly interchangeable — prompts and behaviour differ between them — so a switch still needs re-evaluation, which is exactly what the LangSmith eval datasets we build alongside the system are for.
Building on LangChain?
A technical conversation with the engineers who would do the work. If we are not the right fit, we will say so on the call.