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LangChain

The framework for building LLM apps — chains, agents, RAG, and 600+ integrations.

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Overview

LangChain is the leading framework for building LLM-powered applications, providing composable components for chains, agents, RAG pipelines, and tool integrations.

Key Features

  • LCEL (LangChain Expression Language) for composing chains
  • LangGraph for stateful, multi-actor agent workflows
  • 600+ integrations with LLMs, vector stores, tools
  • RAG primitives: document loaders, splitters, retrievers
  • Memory and conversation history management
  • LangSmith for tracing, debugging, and evaluation

Real-World Workflows

Build a document Q&A bot with RAG

  1. 1Load documents using LangChain document loaders (PDF, web, S3)
  2. 2Split text with RecursiveCharacterTextSplitter
  3. 3Embed and store in a vector database like Chroma or Pinecone
  4. 4Create a RetrievalQA chain and expose it as an API

Build a ReAct agent with tools

  1. 1Define tools (web search, calculator, database query)
  2. 2Use create_react_agent with your tool list and LLM
  3. 3The agent iteratively reasons, calls tools, and observes results
  4. 4Trace execution in LangSmith to debug reasoning steps

Getting Started

pip install langchain langchain-openai

from langchain_openai import ChatOpenAI
from langchain_core.messages import HumanMessage

model = ChatOpenAI(model='gpt-4o')
response = model.invoke([HumanMessage(content='Hello!')])
print(response.content)

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