LangChain - Build LLM Applications with Agents & RAG

The most popular framework for building LLM-powered applications.

When to use LangChain

Use LangChain when:

• Building agents with tool calling and reasoning (ReAct pattern)
• Implementing RAG (retrieval-augmented generation) pipelines
• Need to swap LLM providers easily (OpenAI, Anthropic, Google)
• Creating chatbots with conversation memory
• Rapid prototyping of LLM applications
• Production deployments with LangSmith observability

Metrics:

• 119,000+ GitHub stars
• 272,000+ repositories use LangChain
• 500+ integrations (models, vector stores, tools)
• 3,800+ contributors

Use alternatives instead:

• LlamaIndex: RAG-focused, better for document Q&A
• LangGraph: Complex stateful workflows, more control
• Haystack: Production search pipelines
• Semantic Kernel: Microsoft ecosystem

Quick start

Installation

# Core library (Python 3.10+)
pip install -U langchain

# With OpenAI
pip install langchain-openai

# With Anthropic
pip install langchain-anthropic

# Common extras
pip install langchain-community  # 500+ integrations
pip install langchain-chroma     # Vector store

Basic LLM usage

from langchain_anthropic import ChatAnthropic

# Initialize model
llm = ChatAnthropic(model="claude-sonnet-4-5-20250929")

# Simple completion
response = llm.invoke("Explain quantum computing in 2 sentences")
print(response.content)

Create an agent (ReAct pattern)

from langchain.agents import create_agent
from langchain_anthropic import ChatAnthropic

# Define tools
def get_weather(city: str) -> str:
    """Get current weather for a city."""
    return f"It's sunny in {city}, 72°F"

def search_web(query: str) -> str:
    """Search the web for information."""
    return f"Search results for: {query}"

# Create agent (<10 lines!)
agent = create_agent(
    model=ChatAnthropic(model="claude-sonnet-4-5-20250929"),
    tools=[get_weather, search_web],
    system_prompt="You are a helpful assistant. Use tools when needed."
)

# Run agent
result = agent.invoke({"messages": [{"role": "user", "content": "What's the weather in Paris?"}]})
print(result["messages"][-1].content)

Core concepts

1. Models - LLM abstraction

from langchain_openai import ChatOpenAI
from langchain_anthropic import ChatAnthropic
from langchain_google_genai import ChatGoogleGenerativeAI

# Swap providers easily
llm = ChatOpenAI(model="gpt-4o")
llm = ChatAnthropic(model="claude-sonnet-4-5-20250929")
llm = ChatGoogleGenerativeAI(model="gemini-2.0-flash-exp")

# Streaming
for chunk in llm.stream("Write a poem"):
    print(chunk.content, end="", flush=True)

2. Chains - Sequential operations

from langchain.chains import LLMChain
from langchain.prompts import PromptTemplate

# Define prompt template
prompt = PromptTemplate(
    input_variables=["topic"],
    template="Write a 3-sentence summary about {topic}"
)

# Create chain
chain = LLMChain(llm=llm, prompt=prompt)

# Run chain
result = chain.run(topic="machine learning")

3. Agents - Tool-using reasoning

ReAct (Reasoning + Acting) pattern:

from langchain.agents import create_tool_calling_agent, AgentExecutor
from langchain.tools import Tool

# Define custom tool
calculator = Tool(
    name="Calculator",
    func=lambda x: eval(x),
    description="Useful for math calculations. Input: valid Python expression."
)

# Create agent with tools
agent = create_tool_calling_agent(
    llm=llm,
    tools=[calculator, search_web],
    prompt="Answer questions using available tools"
)

# Create executor
agent_executor = AgentExecutor(agent=agent, tools=[calculator], verbose=True)

# Run with reasoning
result = agent_executor.invoke({"input": "What is 25 * 17 + 142?"})

4. Memory - Conversation history

from langchain.memory import ConversationBufferMemory
from langchain.chains import ConversationChain

# Add memory to track conversation
memory = ConversationBufferMemory()

conversation = ConversationChain(
    llm=llm,
    memory=memory,
    verbose=True
)

# Multi-turn conversation
conversation.predict(input="Hi, I'm Alice")
conversation.predict(input="What's my name?")  # Remembers "Alice"

RAG (Retrieval-Augmented Generation)

Basic RAG pipeline

from langchain_community.document_loaders import WebBaseLoader
from langchain.text_splitter import RecursiveCharacterTextSplitter
from langchain_openai import OpenAIEmbeddings
from langchain_chroma import Chroma
from langchain.chains import RetrievalQA

# 1. Load documents
loader = WebBaseLoader("https://docs.python.org/3/tutorial/")
docs = loader.load()

# 2. Split into chunks
text_splitter = RecursiveCharacterTextSplitter(
    chunk_size=1000,
    chunk_overlap=200
)
splits = text_splitter.split_documents(docs)

# 3. Create embeddings and vector store
vectorstore = Chroma.from_documents(
    documents=splits,
    embedding=OpenAIEmbeddings()
)

# 4. Create retriever
retriever = vectorstore.as_retriever(search_kwargs={"k": 4})

# 5. Create QA chain
qa_chain = RetrievalQA.from_chain_type(
    llm=llm,
    retriever=retriever,
    return_source_documents=True
)

# 6. Query
result = qa_chain({"query": "What are Python decorators?"})
print