Development

LangChain Development Services

LangChain is a framework for building LLM applications out of composable pieces — prompt templates, retrievers, memory, and tool calls — instead of wiring everything to a raw API by hand. We build RAG chains, conversational memory, and multi-step LLM workflows in LangChain as the implementation layer under our RAG and AI Agent services.

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What LangChain Gives You

Composable building blocks for LLM applications

Instead of hand-writing the plumbing between your vector store, your prompt, and the LLM call every time, LangChain provides standard, swappable components — document loaders, retrievers, memory stores, output parsers — that compose into a chain. That composability is what makes it practical to build and iterate on RAG systems and agents quickly.

Chains

Compose retrieval, prompting and parsing into a single reusable pipeline.

Memory

Give conversations context across turns, from simple buffers to summarized long-term memory.

Tools & retrievers

Standardized interfaces for connecting vector stores, APIs, and functions to the LLM.

Our Process

How we build with LangChain

Chain design

Map out the retrieval, prompting and parsing steps the application needs before building.

Component build

Implement retrievers, memory, and tool integrations using LangChain's component interfaces.

Evaluation

Test the chain against real queries and tune retrieval and prompting for accuracy.

Production hardening

Add error handling, timeouts, and observability (LangSmith or custom logging) before launch.

Use Cases

What we build with LangChain

RAG pipelines

The retrieval and generation chain underneath our RAG Development service.

SaaSEnterprise

Conversational agents with memory

Chatbots and assistants that remember context across a conversation, not just a single turn.

Support

Multi-step LLM workflows

Chained prompts and tool calls for tasks that need more than a single LLM response.

Automation

Technology Stack

Tools we use alongside LangChain

LangChain LangSmith GPT-4o Claude 3.5 Pinecone / pgvector FastAPI Python

Benefits

Why LangChain as the implementation layer

Standard, swappable components mean switching a vector store or LLM provider doesn't mean a rewrite.

Faster iteration on retrieval and prompting during development, since components are composable.

Built-in memory abstractions save time building conversational context handling from scratch.

A large, active ecosystem of integrations for vector stores, tools, and document loaders.

FAQs

Frequently asked questions

Is LangChain the same as building a RAG system?

LangChain is the framework we often use to implement RAG chains — the retrieval, prompting and parsing pipeline — but our RAG Development service covers the whole system: data ingestion, vector store setup, evaluation and deployment, not just the chain code.

Why use LangChain instead of calling the LLM API directly?

For simple single-turn calls, a direct API call is fine. LangChain earns its place once you need composable retrieval, memory, or multi-step chains — it standardizes the plumbing so those pieces are swappable and easier to maintain.

Do you also work with LlamaIndex or other frameworks?

Yes — we choose the framework based on the project; LangChain is our most common choice for chains and agents, but LlamaIndex is sometimes a better fit for pure retrieval-focused indexing.

Can you take over an existing LangChain project?

Yes — we regularly review and extend existing LangChain codebases, including debugging retrieval quality issues and adding production hardening.

How is this different from your LangGraph Development service?

LangChain is best for linear or moderately branching chains; LangGraph is built for more complex, stateful, multi-agent workflows with explicit control flow. We recommend based on how complex your workflow's decision logic is.

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