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Bring in a LangChain engineer who builds retrieval and agent workflows you can trace, evaluate and swap between model providers, instead of prompt chains that only work in the demo.

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A cleaner way to hire LangChain developers

Hire LangChain developers from Krapton to build LLM features that hold up after the demo: retrieval-augmented generation over your documents, tool-calling agents that act on your systems, and multi-step workflows with state, retries and...

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24h
Replacement
30d
IP yours
100%

  • Retrieval quality over prompt tweaks

    Most bad RAG answers come from bad retrieval, not the model. A LangChain developer tunes chunking, adds hybrid keyword and vector search, applies metadata filters and rerankers, and measures recall on real questions before anyone rewrites the system prompt again.

  • Agents with guardrails built in

    Tool-calling agents can loop, call the wrong tool or take irreversible actions. Engineers who know LangGraph set recursion limits, validate tool arguments against schemas, add human approval before writes, and persist state with checkpointers so a failed run resumes instead of restarting.

  • Provider choice stays open

    Model pricing, quality and terms change often. Because LangChain wraps chat models and embeddings behind shared interfaces, a well-structured codebase can trial a different provider or a self-hosted model through configuration, then compare results on the same evaluation set before switching.

  • Visible, measurable LLM behavior

    Without tracing and evals, every complaint about a wrong answer becomes guesswork. Our developers instrument chains and graphs with LangSmith, keep curated datasets of real questions, and run regression evaluations whenever prompts, retrievers or models change.

Why LangChain?

What makes LangChain the right choice for modern engineering teams.

  • Chains & LCEL

    LangChain Expression Language for composing prompt templates, LLMs, and output parsers.

  • RAG pipelines

    Document loaders, text splitters, vector stores, and retrievers for grounded generation.

  • Agents & tools

    ReAct, function-calling, and custom agents that use tools to accomplish multi-step tasks.

  • Memory

    Conversation buffer, summary, and entity memory for stateful multi-turn conversations.

  • Multi-provider support

    Works with OpenAI, Anthropic, Google, Mistral, Ollama, and 50+ LLM providers.

  • LangSmith

    Tracing, evaluation, and prompt management for production LLM applications.

What you can build with LangChain

  • Document Q&A assistants

    Question answering over contracts, manuals, policies or tickets, with loaders for PDFs and wikis, chunking tuned to document structure, citations back to the source passage, and access filters so users only retrieve what they may see.

  • Tool-calling business agents

    Agents that query your CRM, create tickets, look up orders or draft replies through typed tools, built on LangGraph with explicit state, retry policies and approval steps before any action that changes data.

  • Structured data extraction

    Pipelines that turn emails, invoices or forms into validated records using with_structured_output and Pydantic schemas, with fallbacks for malformed responses and a review queue for records that fail validation.

  • Natural-language data queries

    Text-to-SQL assistants that answer questions over a reporting database through a read-only connection, with schema descriptions in the prompt, query validation before execution and row limits to protect the warehouse.

  • Support triage workflows

    Incoming tickets classified, routed and answered from the knowledge base when a grounded answer exists, with escalation to a human agent and conversation state stored per thread for follow-up messages.

  • Research and summarization pipelines

    Multi-step graphs that search internal and web sources, summarize findings with map-reduce patterns, and assemble a cited report, with each node traced so reviewers can audit where every claim came from.

Building LangChain systems that survive change

LangChain moves quickly. Packages were split into langchain-core, provider-specific integrations and community modules, older chain classes and memory helpers were deprecated, and agent building shifted toward LangGraph. Teams that copied tutorial code from different periods often end up with a mix of patterns that is hard to upgrade. Krapton’s LangChain developers keep the framework at the edges: business logic, prompts and retrieval settings live in your own modules, LangChain components are wired in thin adapters, and every package version is pinned and upgraded deliberately. Prompts are versioned like code. Retrieval parameters such as chunk size, overlap, top-k and reranker thresholds sit in configuration so they can be tuned against the evaluation set without code changes. The result is a codebase where replacing a vector store or model provider is a contained change rather than a rewrite.

Security and reliability get the same attention. Any agent that reads untrusted text, such as emails, web pages or uploaded files, is exposed to prompt injection, so our engineers limit each tool to the minimum permissions it needs, separate read tools from write tools, and require human confirmation for actions that send messages, move money or delete data. Retrieved documents are filtered by the requesting user’s permissions before they reach the prompt, not after. Personal data is redacted where the use case allows it, and traces are configured so sensitive fields are not stored in plain text. On the reliability side, every model call has a timeout, retries with backoff and a fallback path, and LangGraph checkpoints let long-running workflows resume after a crash. These controls are cheap to add early and expensive to retrofit once an assistant is in front of customers.

LangChain in action

from langchain_openai import ChatOpenAI, OpenAIEmbeddings
from langchain_community.vectorstores import Chroma
from langchain.chains import RetrievalQA
from langchain_core.prompts import ChatPromptTemplate

# RAG pipeline
embeddings = OpenAIEmbeddings(model="text-embedding-3-small")
vectorstore = Chroma.from_documents(docs, embeddings)
retriever = vectorstore.as_retriever(search_kwargs={"k": 5})

prompt = ChatPromptTemplate.from_messages([
    ("system", "Answer using only the provided context.\nContext: {context}"),
    ("human", "{question}"),
])

chain = RetrievalQA.from_chain_type(
    llm=ChatOpenAI(model="gpt-4o"),
    retriever=retriever,
    chain_type_kwargs={"prompt": prompt},
)

answer = chain.invoke({"query": "What is the refund policy?"})

What our LangChain developers know

Every Krapton developer is vetted with real production experience in LangChain across multiple industry domains.

Hire a developerSchedule a call
  • RAG architectureBuilding retrieval-augmented generation pipelines with vector stores and hybrid search.
  • Prompt engineeringWriting effective system prompts, few-shot examples, and output parsers.
  • Agent designBuilding function-calling agents with tool use, error handling, and plan-and-execute loops.
  • Vector storesIntegrating with Pinecone, Chroma, Weaviate, and pgvector for document retrieval.
  • StreamingImplementing streaming responses for real-time user feedback in chat applications.
  • EvaluationUsing LangSmith and RAGAS for evaluating retrieval quality and answer accuracy.

What our LangChain developers go deep on

  • Retrieval pipeline design

    Chunking strategies matched to document structure, embedding model selection, hybrid BM25 and vector search, metadata filtering, multi-query and parent-document retrieval, and cross-encoder rerankers measured against a labeled question set.

  • LangGraph state and persistence

    Typed graph state, conditional edges, subgraphs, checkpointers backed by Postgres or Redis, interrupts for human-in-the-loop approval, and time-travel debugging to replay a run from any saved step.

  • Tool calling and structured output

    Tools defined with typed schemas, argument validation, timeouts and idempotent side effects, plus with_structured_output for reliable JSON that downstream code can trust without fragile regex parsing.

  • Vector store integration

    pgvector, Pinecone, Weaviate, Qdrant and Chroma, chosen by data volume, filtering needs and hosting rules, with re-indexing jobs for changed documents and deletion handling so retracted content stops appearing.

  • Evaluation with LangSmith and ragas

    Curated datasets from production traces, LLM-as-judge and heuristic evaluators, Ragas metrics such as faithfulness and context recall, and evaluation runs wired into CI before prompt or model changes merge.

  • Streaming and production serving

    Token and event streaming to chat interfaces, async execution for concurrent requests, a managed LangGraph deployment or a FastAPI service, rate-limit handling with backoff, and fallbacks between model providers when one is degraded.

Common LangChain projects we deliver

  • Fixing a RAG bot that hallucinates

    Users report confident wrong answers. The LangChain developer builds a test set from real queries, measures retrieval recall separately from answer faithfulness, then adjusts chunking, adds a reranker or hybrid search, and tightens the prompt to refuse when context is missing.

  • Turning a prototype agent into LangGraph

    An agent built from a quick tutorial works in demos but fails unpredictably. The engineer rebuilds it as a LangGraph state graph with explicit nodes, typed state, checkpointed persistence and interrupts for human review, making failures reproducible and recoverable.

  • Upgrading from legacy chains and memory

    Code written against deprecated chain classes and ConversationBufferMemory is awkward to maintain on newer releases. The developer migrates it to current agent and LangGraph patterns, moves conversation history into a checkpointer, and pins package versions across langchain-core and provider integrations.

  • Cutting LLM spend in production pipelines

    Token costs climb as usage grows. The engineer traces where tokens go, caches repeated calls, routes simple steps to smaller models, trims retrieved context, and confirms with the evaluation set that answer quality holds after each change.

LangChain solutions by industry

  • Legal

    Clause search and contract review assistants that cite the exact passage, respect matter-level access controls, and flag uncertainty instead of inventing case law or obligations.

  • Insurance

    Claims and policy document extraction, coverage question answering for adjusters, and triage workflows where every automated decision is logged with the retrieved evidence behind it.

  • SaaS products

    In-app assistants that answer from product documentation and perform actions through your own API, with per-tenant retrieval filters so one customer’s data never appears in another’s answers.

  • Healthcare

    Clinical guideline lookup and administrative document summarization, built with PII redaction before model calls and deployments that keep protected data inside approved cloud regions.

LangChain vs LlamaIndex

The two frameworks overlap heavily and can even be combined. LlamaIndex started with a sharper focus on indexing and retrieval, with many index types and query engines ready to use. LangChain, together with LangGraph, is broader: agents, tool calling, stateful workflows and a large integration catalog. For retrieval-heavy products either works; for complex agent orchestration, LangGraph gives more explicit control over state and flow.

LangChain vs LlamaIndex
DimensionLangChainLlamaIndex
Core focusBroad LLM app framework plus LangGraph orchestrationData indexing, retrieval and query engines
Agent supportLangGraph graphs with checkpoints and human interruptsAgents and event-driven Workflows for orchestration
Retrieval toolingLoaders, splitters, retrievers; you assemble the piecesRich index types and retrievers available out of the box
IntegrationsVery large catalog of providers and vector storesLarge catalog, plus LlamaHub connectors and LlamaParse
ObservabilityLangSmith tracing, datasets and evaluationsCallback integrations with third-party tracing tools
Learning curveMany abstractions; version churn needs pinned dependenciesQuicker start for pure retrieval use cases

How to hire LangChain developers in 5 steps

A transparent, low-risk process — from first call to onboarded engineer in about two business days.

  1. 01Day 0

    Share your brief

    Tell us the stack, scope, seniority and the timezone overlap you need on a free 30-minute discovery call. No obligation, no sales pitch.

  2. 02Within 24 hours

    Review matched profiles

    We shortlist 2–3 pre-vetted LangChain engineers from our in-house bench, with CVs and directly relevant work — usually within one business day.

  3. 03Days 1–2

    Interview & test

    Interview each developer yourself, run a paid trial task, or do a live pairing session. You decide who joins — we never push a profile.

  4. 04Within 2 days

    Onboard securely

    Sign a free NDA, then plug the developer into your Jira, repository and Slack. Engagements are month-to-month with no lock-in.

  5. 05Ongoing

    Scale or replace

    Scale the team up or down month by month as the roadmap changes. If the fit is not right, you get a free replacement within the first 30 days.

More AI / ML technologies

Other AI / ML technologies we work with at Krapton.

Three ways to hire LangChain developers

Pick the engagement that matches how you actually work. No multi-year contracts — scale up or down month by month.

  • Dedicated developer

    Most popular

    Full-time LangChain engineer who reports only to you. Best for ongoing products, long-term roadmaps and teams that need a core hire without the HR overhead.

    • 40 hours / week
    • Your Jira, your repo
    • Month-to-month

  • Hourly / time & materials

    Pay only for billable hours. Ideal for research spikes, code audits, or variable-load LangChain work where scope is still being discovered.

    • Weekly timesheets
    • Slack-first comms
    • No minimum commit

  • Fixed-price milestones

    Scoped delivery with clear milestones and acceptance criteria. Best for well-defined LangChain builds like an MVP, a migration or a specific module.

    • Scope locked upfront
    • Milestone acceptance
    • Predictable budget

Compare ways to hire LangChain developers

Pick the model that matches how you work. Switch between them as your LangChain project evolves — no multi-year contracts.

Compare ways to hire LangChain developers
EngagementDedicated developer (popular)Hourly / time & materialsFixed-price milestones
Best forOngoing products & long-term roadmapsExploratory or variable-load workWell-scoped MVPs, modules & migrations
BillingMonthly, full-time (~160 hrs)Per billable hourPer accepted milestone
CommitmentMonth-to-month, no lock-inNo minimum commitmentScope locked up front
FlexibilityScale up or down monthlyHighest — pay for what you useLowest — defined deliverables
You manageDay-to-day in your toolsWeekly prioritiesAcceptance criteria only
ReportingDaily standupsWeekly timesheetsMilestone demos

Hire LangChain developers from anywhere

Our New Delhi team partners with founders and engineering leaders across eight key markets, with enough daily timezone overlap to feel in-house.

  • Hire LangChain developers in India

    Full working-day overlap

    Krapton is an on-shore LangChain studio in New Delhi. Hire LangChain developers in India and get senior, full-time engineers at roughly 50–70% of US/UK rates — with English-fluent communication and IST working hours.

  • Hire LangChain developers in USA

    4–6 hours of daily overlap — your morning, our evening

    Hire dedicated LangChain developers for your USA team without the local price tag. US companies work with our vetted, full-time LangChain engineers at 50–70% lower cost than local contractors, with enough daily overlap for live standups, pairing and code review.

  • Hire LangChain developers in UK

    Overlap across your whole morning (our afternoon)

    Hire dedicated LangChain developers for your UK team without the local price tag. UK companies work with our vetted, full-time LangChain engineers at 50–70% lower cost than local contractors, with enough daily overlap for live standups, pairing and code review.

  • Hire LangChain developers in Canada

    4–6 hours of daily overlap with ET and PT

    Hire dedicated LangChain developers for your Canada team without the local price tag. Canada companies work with our vetted, full-time LangChain engineers at 50–70% lower cost than local contractors, with enough daily overlap for live standups, pairing and code review.

  • Hire LangChain developers in Australia

    Strong overlap — our morning is your afternoon

    Hire dedicated LangChain developers for your Australia team without the local price tag. Australia companies work with our vetted, full-time LangChain engineers at 50–70% lower cost than local contractors, with enough daily overlap for live standups, pairing and code review.

  • Hire LangChain developers in UAE

    Near-complete overlap — just 90 minutes apart

    Hire dedicated LangChain developers for your UAE team without the local price tag. UAE companies work with our vetted, full-time LangChain engineers at 50–70% lower cost than local contractors, with enough daily overlap for live standups, pairing and code review.

  • Hire LangChain developers in Germany

    Overlap through your morning and our afternoon (DACH)

    Hire dedicated LangChain developers for your Germany team without the local price tag. Germany companies work with our vetted, full-time LangChain engineers at 50–70% lower cost than local contractors, with enough daily overlap for live standups, pairing and code review.

  • Hire LangChain developers in Singapore

    Near-complete overlap — only 2.5 hours apart

    Hire dedicated LangChain developers for your Singapore team without the local price tag. Singapore companies work with our vetted, full-time LangChain engineers at 50–70% lower cost than local contractors, with enough daily overlap for live standups, pairing and code review.

LangChain developer rates

Hire dedicated LangChain developers from Krapton's team in India. Rates below are blended hourly bands; dedicated developers are billed monthly with no lock-in.

Get a tailored quote

  • Junior (1–3 yrs)

    Feature work under senior review

    $30–$40/hr

  • Mid-level (3–6 yrs)

    Owns modules end-to-end

    $40–$50/hr

  • Senior / lead (6+ yrs)

    Architecture, reviews, mentoring

    $50–$60/hr

Why teams hire LangChain developers from Krapton

Projects delivered
50+
In-house engineers
60+
Average client rating
4.8/5
On-time delivery
98%

  • Vetted, full-time engineers

    Every LangChain developer is a full-time, in-house Krapton engineer — never a freelancer or subcontractor.

  • Ready in 24 hours

    Shortlisted LangChain CVs within a business day and an onboarded developer within two.

  • Clear hourly rates

    Published hourly bands by seniority, billed monthly, with no lock-in.

  • Your IP, your repos

    Full IP assignment, free NDA on request, and all code in your accounts from day one.

  • 30-day replacement

    Not the right fit? Free replacement within five business days in the first 30 days.

  • Real timezone overlap

    4–6 hours of daily overlap with US, UK, EU, UAE and AU teams for live collaboration.

Services that pair well with LangChain

Most LangChain engagements also benefit from these Krapton services. Browse full offerings on the services page.

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Hiring LangChain developers — answered

Practical answers to the questions CTOs and founders ask us most often before they hire.

How quickly can I hire a LangChain developer from Krapton?

Within 24 hours of the initial call. We keep a bench of pre-vetted LangChain engineers, so once you confirm the brief we line up two or three shortlisted CVs within a business day and can onboard the selected developer within two.

How much does it cost to hire a dedicated LangChain developer from India?

Blended hourly rates for senior LangChain engineers typically fall in the $30–$60 USD range depending on seniority and engagement length. Dedicated developers are billed monthly with no lock-in.

Are your LangChain developers full-time employees or freelancers?

Every LangChain developer you hire through Krapton is a full-time, in-house engineer — we never subcontract to freelancers or agencies. That means consistent code quality, signed NDAs, and a single point of accountability for your project.

Who owns the code and IP my LangChain developers write?

You do — 100%. IP assignment and confidentiality are covered by the contract and a free NDA on request. All code lives in your repositories, on your infrastructure, under your accounts from day one.

What should I look for when I hire LangChain developers?

Look for engineers who can explain why a RAG answer is wrong before changing anything. A senior LangChain developer understands the Runnable interface and LCEL, builds stateful agents in LangGraph, designs retrieval with hybrid search and reranking, and writes evaluations in LangSmith or Ragas. They should also know when to bypass the framework and call a provider SDK directly, and how to pin and upgrade fast-moving LangChain packages.

Should we use LangChain or LlamaIndex for a RAG project?

Either can deliver a good RAG system, so the deciding factors are what surrounds retrieval. If the product is mostly search and question answering over documents, LlamaIndex offers more ready-made indexing and query patterns. If retrieval is one step inside agents, tool calls and approval flows, LangChain with LangGraph is usually the better fit. Some teams use LlamaIndex for ingestion and LangGraph for orchestration, which works when the boundary is kept clean.

Can you migrate our app off LangChain, or onto it?

Yes, in either direction, and the effort depends on how much logic is tangled with framework classes. Moving onto LangChain usually means wrapping existing prompts and retrieval code in Runnables and adding LangGraph where state is needed. Moving off it means replacing chains with direct SDK calls and your own retrieval module. In both cases the evaluation set is built first, so the new version is proven to answer at least as well as the old one.

How do you test and secure a LangChain application?

Testing combines deterministic unit tests with evaluation runs. Tools, parsers and retrieval filters get normal unit tests with mocked model calls, while end-to-end behavior is scored against a curated dataset in LangSmith on every significant change. Security work covers prompt injection, least-privilege tools, permission-aware retrieval, secrets kept out of prompts and traces, and output validation before any response triggers an action or reaches a user.

How do you handle LangChain version upgrades?

Carefully, because LangChain releases have moved, renamed and deprecated APIs more often than most libraries. The developer pins langchain, langchain-core, LangGraph and each provider package together, reads the migration notes, and upgrades on a branch. The test suite and the evaluation dataset then confirm behavior is unchanged. The 1.0 releases of LangChain and LangGraph slimmed the core package and built agents on LangGraph, so older codebases often need targeted refactoring.

Keep exploring

More ways to plan your LangChain build and team with Krapton.

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