LANGCHAIN · LLM FRAMEWORK · PRODUCTION AI

LangChain Development India — Build AI That Actually Works

We build production-grade AI applications using LangChain — RAG systems, intelligent agents, document processing, and LLM workflows that handle real enterprise workloads.

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How LangChain Works

LangChain Pipeline Architecture

LangChain chains together LLMs, vector stores, tools, and memory into powerful AI applications.

User Input

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Document Retriever

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Context Builder

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LLM (GPT/Claude)

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Output Parser

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Response

LangChain Services

What We Build with LangChain

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RAG Applications

Document Q&A, knowledge bases, and search systems using LangChain's retrieval chains with Pinecone, Weaviate, or pgvector.

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LangChain Agents

AI agents that use LangChain tools to browse the web, query databases, call APIs, and execute multi-step tasks autonomously.

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Conversational AI with Memory

Chatbots with persistent conversation memory using LangChain's memory modules — ConversationBufferMemory, VectorStoreMemory, and Redis memory.

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Document Processing Pipelines

Ingest, chunk, embed, and retrieve from PDFs, Word docs, websites, and databases using LangChain document loaders and text splitters.

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LLM Chains & LCEL

Complex multi-step AI pipelines using LangChain Expression Language (LCEL) — parallel execution, fallbacks, retries, and streaming.

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LangSmith Observability

We set up LangSmith tracing and evaluation so you can monitor every LLM call, debug prompt failures, and measure response quality in production.

FAQ

LangChain Development Questions

What is LangChain used for? +

LangChain is a Python framework for building LLM-powered applications. It provides components for RAG (Retrieval Augmented Generation), AI agents with tool use, conversational memory, document loaders, vector store integration, and multi-LLM orchestration in production environments.

LangChain vs LlamaIndex — which should I use? +

LangChain is better for building complex AI agents with tool use and multi-step workflows. LlamaIndex is better for pure document indexing and retrieval. For most enterprise applications, we use both together — LlamaIndex for document ingestion, LangChain for agent orchestration and chains.

Is LangChain production-ready? +

Yes. LangChain is used in production by thousands of companies globally. We have deployed LangChain-based systems handling millions of queries per month. Key production requirements: Redis caching, structured error handling, LangSmith tracing, async execution, and proper rate limit management.

Can LangChain work with on-premise LLMs? +

Yes. LangChain supports on-premise LLMs via Ollama, vLLM, HuggingFace Text Generation Inference, and custom OpenAI-compatible endpoints. We deploy local Llama 3 or Mistral models and integrate them seamlessly into LangChain applications.