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(result["result"])
print(f"Sources: {result['source_documents']}")
Conversational RAG with memory
from langchain.chains import ConversationalRetrievalChain
# RAG with conversation memory
qa = ConversationalRetrievalChain.from_llm(
llm=llm,
retriever=retriever,
memory=ConversationBufferMemory(
memory_key="chat_history",
return_messages=True
)
)
# Multi-turn RAG
qa({"question": "What is Python used for?"})
qa({"question": "Can you elaborate on web development?"}) # Remembers context
Advanced agent patterns
Structured output
from langchain_core.pydantic_v1 import BaseModel, Field
# Define schema
class WeatherReport(BaseModel):
city: str = Field(description="City name")
temperature: float = Field(description="Temperature in Fahrenheit")
condition: str = Field(description="Weather condition")
# Get structured response
structured_llm = llm.with_structured_output(WeatherReport)
result = structured_llm.invoke("What's the weather in SF? It's 65F and sunny")
print(result.city, result.temperature, result.condition)
Parallel tool execution
from langchain.agents import create_tool_calling_agent
# Agent automatically parallelizes independent tool calls
agent = create_tool_calling_agent(
llm=llm,
tools=[get_weather, search_web, calculator]
)
# This will call get_weather("Paris") and get_weather("London") in parallel
result = agent.invoke({
"messages": [{"role": "user", "content": "Compare weather in Paris and London"}]
})
Streaming agent execution
# Stream agent steps
for step in agent_executor.stream({"input": "Research AI trends"}):
if "actions" in step:
print(f"Tool: {step['actions'][0].tool}")
if "output" in step:
print(f"Output: {step['output']}")
Common patterns
Multi-document QA
from langchain.chains.qa_with_sources import load_qa_with_sources_chain
# Load multiple documents
docs = [
loader.load("https://docs.python.org"),
loader.load("https://docs.numpy.org")
]
# QA with source citations
chain = load_qa_with_sources_chain(llm, chain_type="stuff")
result = chain({"input_documents": docs, "question": "How to use numpy arrays?"})
print(result["output_text"]) # Includes source citations
Custom tools with error handling
from langchain.tools import tool
@tool
def risky_operation(query: str) -> str:
"""Perform a risky operation that might fail."""
try:
# Your operation here
result = perform_operation(query)
return f"Success: {result}"
except Exception as e:
return f"Error: {str(e)}"
# Agent handles errors gracefully
agent = create_agent(model=llm, tools=[risky_operation])
LangSmith observability
import os
# Enable tracing
os.environ["LANGCHAIN_TRACING_V2"] = "true"
os.environ["LANGCHAIN_API_KEY"] = "your-api-key"
os.environ["LANGCHAIN_PROJECT"] = "my-project"
# All chains/agents automatically traced
agent = create_agent(model=llm, tools=[calculator])
result = agent.invoke({"input": "Calculate 123 * 456"})
# View traces at smith.langchain.com
Vector stores
Chroma (local)
from langchain_chroma import Chroma
vectorstore = Chroma.from_documents(
documents=docs,
embedding=OpenAIEmbeddings(),
persist_directory="./chroma_db"
)
Pinecone (cloud)
from langchain_pinecone import PineconeVectorStore
vectorstore = PineconeVectorStore.from_documents(
documents=docs,
embedding=OpenAIEmbeddings(),
index_name="my-index"
)
FAISS (similarity search)
from langchain_community.vectorstores import FAISS
vectorstore = FAISS.from_documents(docs, OpenAIEmbeddings())
vectorstore.save_local("faiss_index")
# Load later
vectorstore = FAISS.load_local("faiss_index", OpenAIEmbeddings())
Document loaders
# Web pages
from langchain_community.document_loaders import WebBaseLoader
loader = WebBaseLoader("https://example.com")
# PDFs
from langchain_community.document_loaders import PyPDFLoader
loader = PyPDFLoader("paper.pdf")
# GitHub
from langchain_community.document_loaders import GithubFileLoader
loader = GithubFileLoader(repo="user/repo", file_filter=lambda x: x.endswith(".py"))
# CSV
from langchain_community.document_loaders import CSVLoader
loader = CSVLoader("data.csv")
Text splitters
# Recursive (recommended for general text)
from langchain.text_splitter import RecursiveCharacterTextSplitter
splitter = RecursiveCharacterTextSplitter(
chunk_size=1000,
chunk_overlap=200,
separators=["\n\n", "\n", " ", ""]
)
# Code-aware
from langchain.text_splitter import PythonCodeTextSplitter
splitter = PythonCodeTextSplitter(chunk_size=500)
# Semantic (by meaning)
from langchain_experimental.text_splitter import SemanticChunker
splitter = SemanticChunker(OpenAIEmbeddings())
Best practices
- Start simple - Use
create_agent() for most cases
- Enable streaming - Better UX for long responses
- Add error handling - Tools can fail, handle gracefully
- Use LangSmith - Essential for debugging agents
- Optimize chunk size - 500-1000 chars for RAG
- Version prompts - Track changes in production
- Cache embeddings - Expensive, cache when possible
- Monitor costs - Track token usage with LangSmith
Performance benchmarks
| Operation | Latency | Notes |
|---|
| Simple LLM call | ~1-2s | Depends on provider |
| Agent with 1 tool | ~3-5s | ReAct reasoning overhead |
| RAG retrieval | ~0.5-1s | Vector search + LLM |
| Embedding 1000 docs | ~10-30s | Depends on model |
LangChain vs LangGraph
| Feature | LangChain | LangGraph |
|---|
| Best for | Quick agents, RAG | Complex workflows |
| Abstraction level | High | Low |
| Code to start | <10 lines | ~30 lines |
| Control | Simple | Full control |
| Stateful workflows | Limited | Native |
| Cyclic graphs | No | Yes |
| Human-in-loop | Basic | Advanced |
Use LangGraph when:
- Need stateful workflows with cycles
- Require fine-grained control
- Building multi-agent systems
- Production apps with complex logic
References
- Agents Guide - ReAct, tool calling, streaming
- RAG Guide - Document loaders, retrievers, QA chains
- Integration Guide - Vector stores, LangSmith, deployment
Resources
