LangChainBuild AI Apps Fast
LangChain is the most popular framework for building applications with LLMs. It gives you the building blocks to create chatbots, agents, and AI-powered workflows — connecting language models to your data, tools, and the real world.
What Is LangChain?
What is LangChain?
LangChain is a framework that makes it easy to build applications with LLMs (like GPT-4 or Claude). Instead of writing everything from scratch, LangChain gives you pre-built pieces — like Lego blocks for AI apps.
Why do I need it?
LLMs alone can only respond to prompts. LangChain lets them DO things: search the web, query databases, remember conversations, use tools, and make decisions. It turns a chatbot into an AI agent.
Is it hard to learn?
The basics are surprisingly simple. You can build a working chatbot or document Q&A system in under an hour. The framework handles the complex stuff — you focus on what your app should do.
Think of it like: LangChain is to AI apps what React is to web apps. A framework that handles the boring stuff so you can focus on what makes your app unique.
Core Components
The building blocks of every LangChain application.
Models
Connect to any LLM: OpenAI, Anthropic, Google, open-source, or local models. Swap between them easily.
Prompts
Templates for your prompts with variables. Reuse, compose, and version your prompts professionally.
Chains
Connect steps together. Output of one step becomes input to the next. Build complex workflows simply.
Memory
Give your AI a memory. Remember past conversations, user preferences, or session context.
Agents
AI that decides what to do. Agents choose which tools to use based on the task at hand.
Tools
Give AI abilities: search Google, run code, query APIs, send emails, access databases.
How Chains Work
A simple RAG (Retrieval-Augmented Generation) chain in action.
User Asks a Question
"What were Apple's earnings last quarter?"
Chain Retrieves Data
First step searches your documents or database for relevant info.
Chain Adds Context
Retrieved info is added to the prompt: "Given this data: [earnings report]..."
LLM Generates Answer
The AI uses the context to give an accurate, grounded answer.
Simple Example
A complete LangChain app in 10 lines of Python.
Python
from langchain_openai import ChatOpenAI
from langchain_core.prompts import ChatPromptTemplate
from langchain_core.output_parsers import StrOutputParser
# 1. Create the model
model = ChatOpenAI(model="gpt-4o")
# 2. Create a prompt template
prompt = ChatPromptTemplate.from_template(
"Explain {topic} to a 5-year-old in 2 sentences."
)
# 3. Chain them together
chain = prompt | model | StrOutputParser()
# 4. Run it!
result = chain.invoke({"topic": "quantum computing"})
print(result)The | operator chains components together. Output flows from left to right.
What Can You Build?
Real applications people are building with LangChain.
Document Q&A
Upload PDFs, docs, or websites. Ask questions and get answers from YOUR data, not the internet.
Vector Store • Embeddings • Retriever
Customer Support Bot
A chatbot that knows your products, policies, and can escalate to humans when needed.
Memory • Tools • Agents
Data Analyst Agent
Upload a CSV. Ask "What were sales by region?" and get charts and insights automatically.
Python REPL • Pandas • Agents
Research Assistant
Search the web, summarize articles, extract facts, and compile reports automatically.
Web Search • Summarization • Chains
Code Generator
Describe what you want. Get working code that's tested and debugged by the AI.
Code Execution • Error Handling • Iteration
Workflow Automation
Connect to Slack, email, calendars. "Schedule a meeting with John about the project update."
API Integrations • Agents • Memory
Getting Started
From zero to your first LangChain app.
Install LangChain
Python package. Also available for JavaScript/TypeScript.
Command
pip install langchain langchain-openai
Set Your API Key
Or use Anthropic, Google, or local models.
Command
export OPENAI_API_KEY="sk-..."
Try the Quickstart
Build your first chain in 10 lines of code.
Command
See example code above
Explore Templates
Start from pre-built application templates.
Command
langchain app new my-app --package rag-conversation
The LangChain Ecosystem
LangChain is more than just the core library.
LangChain Core
The main framework. Models, prompts, chains, agents.
LangServe
Deploy your chains as REST APIs with one command.
LangSmith
Debug, test, and monitor your LLM applications in production.
LangGraph
Build complex, stateful agent workflows with cycles and branches.
Alternatives to Consider
LangChain is great, but other options exist for specific needs.
LlamaIndex
Data indexing and retrieval (RAG-focused)
Haystack
Search and question answering pipelines
Semantic Kernel
Microsoft's AI orchestration (C#, Python)
Vercel AI SDK
React/Next.js AI streaming and components