LangChain Training India — Build LLM Applications That Work in Production
LangChain has 90,000+ GitHub stars and powers most of the production AI applications built in India today. Your team knowing it exists is not the same as your team being able to ship with it. This training closes that gap.
What is LangChain?
LangChain is the most widely used open-source framework for building applications powered by large language models. It provides standardised, composable interfaces for connecting LLMs to your data, tools, memory, and external APIs — removing the boilerplate of raw API calls and replacing it with a reliable, production-tested abstraction layer.
Think of it this way: you can talk to an LLM directly via its API. LangChain is what you use when that LLM needs to search your documents, call your APIs, remember what happened three messages ago, and make decisions across multiple steps. It is the glue layer between the model and the rest of your system.
LangGraph extends LangChain for agentic use cases — where the AI needs to loop, branch, and maintain state across a complex multi-step workflow. If LangChain is how you build pipelines, LangGraph is how you build autonomous agents.
LangChain vs LlamaIndex — which does your team need?
The honest answer: probably both. Here's how they split.
| Feature | LangChain + LangGraph | LlamaIndex |
|---|---|---|
| Best for | Full LLM application orchestration | Document ingestion & retrieval |
| Agent support | ✅ LangGraph (stateful agents) | ⚠️ Basic (QueryEngine agents) |
| RAG pipeline | ✅ Chains + retrievers | ✅ Native indexing strength |
| Production tooling | ✅ LangSmith tracing | ⚠️ Limited built-in |
| Community | ✅ Larger, more examples | ✅ Strong for data-heavy RAG |
| Use in production | Orchestration layer | Retrieval layer |
Our Production AI Engineering course covers both LangChain and LlamaIndex — and explains exactly when to use each in a real-world project.
What LangChain training covers
Every topic is taught hands-on — no slides-only sessions. Every concept is immediately coded in a project your team owns.
LangChain Core Concepts
Chains, prompts, output parsers, memory, and document loaders — the full LangChain object model understood, not just copied from docs.
RAG with LangChain
Build end-to-end RAG pipelines using LangChain + vector databases. Chunking, embedding, retrieval, reranking — every step in code.
LangGraph Agents
Build stateful agents using LangGraph. ReAct loops, tool-calling, multi-step reasoning, and agent memory management.
Tool Calling & Integrations
Connect agents to real APIs, databases, and internal tools using LangChain tool interfaces and function calling.
Evaluation & Tracing
Use LangSmith for tracing, debugging, and evaluating LangChain applications. Build evaluation pipelines before you ship.
Production Patterns
Async execution, streaming, error handling, cost management, and deployment — the production practices missing from most tutorials.
LangChain Training — Frequently Asked Questions
What is LangChain?+
LangChain is an open-source Python and JavaScript framework for building applications powered by large language models (LLMs). It provides standardised interfaces for connecting LLMs to data sources, tools, memory, and external APIs. LangChain is the most widely used LLM framework in production — it abstracts the complexity of orchestrating prompts, retrieval, and model calls into composable chains and agents.
LangChain vs LlamaIndex — which should my team learn?+
LangChain is better for building full LLM applications: chains, agents, tool use, memory, and multi-step workflows. LlamaIndex is better for data ingestion and retrieval — indexing large document collections and querying them efficiently. In production, most teams use both: LlamaIndex for the RAG retrieval layer, LangChain for the application orchestration layer. Our training covers both and explains when to use each.
What is LangGraph and how does it relate to LangChain?+
LangGraph is a library built on top of LangChain that adds stateful, cyclic, graph-based workflow execution. While LangChain handles linear chains of steps, LangGraph handles agents that need to loop, branch, reflect, and maintain state across many steps. LangGraph is the production-grade standard for building autonomous AI agents in 2025–2026. Our training covers both LangChain and LangGraph as a connected system.
What Python level is needed for LangChain training?+
Intermediate Python — comfortable writing functions, classes, and making API calls with the requests library. You do not need prior ML or LLM framework experience. If your team can build a simple REST API in Python, they are ready for LangChain training.
Bring LangChain training to your team
Tell us your team's current Python level and what you want to build. We'll send a proposal within 24 hours.
LangChain is covered in depth as part of the full Production AI Engineering programme.