LangChain development company
Krapton builds retrieval systems and agents with LangChain and LangGraph, then checks them against LangSmith traces and evaluation datasets, so answers stay grounded, tool calls stay safe and model swaps do not break production.
- Senior engineers · IST + EST overlap
- NDA on day 1
- 24-hour reply
- Projects shipped
- 50+
- Code ownership
- 100%
- Client rating
- 4.8/5
- First reply
- 24h
LangChain development services for RAG and agents
Krapton is a LangChain development company that builds LLM features meant to survive real users: retrieval-augmented generation over your documents, agents that call your APIs, and multi-step workflows with human approval points. LangChain gives us provider-neutral interfaces for chat models, embeddings, retrievers and tools, while LangGraph adds what production agents need: explicit state, checkpointers for resumable runs, interrupts for human review and controlled loops instead of open-ended reasoning. The engineering effort sits in the parts demos skip. We tune chunking and metadata, combine vector and keyword search with a reranker, enforce structured output with schemas, cap tool permissions, and trace every step in LangSmith so failures can be replayed. Before launch, each pipeline is scored against an evaluation dataset of real questions, and the same dataset runs in CI whenever prompts, models or retrievers change. For a single prompt and response, we may advise calling the provider SDK directly instead.
LangChain development services
RAG pipeline development
Document loaders, text splitters, embeddings and vector stores such as pgvector, Pinecone or Qdrant, with hybrid search, reranking and citations so every answer points back to its source passage.
LangGraph agent development
Stateful agents modeled as graphs with typed state, tool nodes, retries and interrupts for human approval, persisted through Postgres or Redis checkpointers so long tasks survive restarts.
Tool and API integration
We expose your internal APIs, databases and SaaS systems as typed tools or MCP servers, with input validation, scoped credentials and audit logs for every action an agent takes.
LangSmith evaluation and tracing
Datasets built from real user questions, LLM-as-judge and rule-based evaluators, regression runs in CI, and production tracing that links each bad answer to the retrieval and prompt that caused it.
Legacy chain upgrades
Older LLMChain, ConversationalRetrievalChain and memory-class code is migrated to current LangChain and LangGraph APIs, with evaluation runs before and after to confirm behavior holds.
Model routing and fallbacks
Requests route between providers by task, cost or latency, with fallbacks on rate limits and outages, response caching, and token budgets enforced per user or tenant.
Why teams choose Krapton for LangChain
Senior engineers, fixed scope, and code you own — the things agencies promise and rarely deliver.
Evaluation datasets from day one
Every project starts with a set of real questions and expected answers, so improvements to prompts, chunking or models are measured rather than judged by a quick demo.
Bounded agent design
Agents get the narrowest tools that do the job, explicit step limits and approval gates for writes, which keeps costs predictable and limits damage from prompt injection.
Provider-neutral architecture
Because models sit behind LangChain interfaces, switching between OpenAI, Anthropic, Gemini or a self-hosted model is a configuration change followed by an evaluation run, not a rewrite.
Pragmatic about abstraction
We use LangChain where its integrations save time and drop to provider SDKs where an abstraction hides important behavior, documenting each choice for your team.
How a LangChain project runs with Krapton
01
Question and source mapping
We collect real user questions, map them to source documents and systems, and agree what a correct, grounded answer looks like for each category.
02
Prototype with evals
A thin LangChain or LangGraph prototype runs against the evaluation set early, exposing retrieval gaps and prompt failures before the interface is built.
03
Harden for production
We add guardrails, structured output validation, streaming, rate limiting, caching and LangSmith tracing, then red-team the system with injection and edge-case prompts.
04
Launch and iterate
After release, low-scoring traces feed new dataset examples, and every prompt or model change must pass the regression suite before it ships.
The LangChain stack we build with
LangChain development: questions answered
What does a LangChain development company build?
Mostly two things: retrieval systems that answer from your own data and agents that take actions through your tools. A LangChain development company designs the retrieval pipeline, prompts, tool schemas and agent graph, then adds evaluation, tracing, security controls and deployment. Krapton also delivers the evaluation dataset and LangSmith project, so your team can keep measuring quality after handover.
Do we need LangChain, or can we call the model API directly?
Call the API directly if the feature is a single prompt with no retrieval or tools. LangChain earns its place once you need several providers, many integrations, retrieval components, or agent state and human review through LangGraph. The tradeoff is an extra dependency with its own release cycle. We pin versions, wrap LangChain behind your own interfaces, and keep critical paths simple enough to replace if needed.
How do you reduce hallucinations in a LangChain RAG system?
By fixing retrieval first, because many wrong answers start with the wrong context. We test chunk sizes and overlap, add metadata filters, combine dense and keyword search, and rerank results before generation. Prompts require citations and allow the model to say it does not know. LangSmith evaluators then score faithfulness and answer relevance on every change, and low-confidence answers can route to a human.
Can you upgrade an older LangChain codebase?
Yes. Older projects often rely on LLMChain, AgentExecutor and memory classes that moved to the langchain-classic package or were replaced when LangChain and LangGraph reached 1.0. We first capture current behavior in an evaluation dataset, then migrate to create_agent, LCEL runnables or LangGraph graphs, and compare scores before and after so the upgrade does not quietly change answers. Integration packages such as langchain-openai are versioned separately, so we pin each one.
What affects the cost of LangChain development services?
Scope and data readiness affect it most. Drivers include how many document sources need loaders and cleaning, whether agents write to external systems and need approval flows, the number of tools and integrations, evaluation depth, compliance requirements such as PII redaction or data residency, and expected traffic. Model and vector database usage are billed by providers separately; we estimate token spend per request during design.
Ready to build? Tell us what you have in mind.
Send a short brief and a senior engineer will reply within one business day with next steps — no sales fluff.