langchain

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:

• 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
from scripts.safe_arithmetic import calculate

# Define custom tool
calculator = Tool(
    name="Calculator",
    func=lambda x: str(calculate(x)),
    description="Useful for arithmetic with numbers and +, -, *, /."
)

# 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)

Details, code examples and parameter tables: references/rag-retrieval-augmented-generation.md. Read it when this step applies.

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

1. Start simple - Use create_agent() for most cases
2. Enable streaming - Better UX for long responses
3. Add error handling - Tools can fail, handle gracefully
4. Use LangSmith - Essential for debugging agents
5. Optimize chunk size - 500-1000 chars for RAG
6. Version prompts - Track changes in production
7. Cache embeddings - Expensive, cache when possible
8. 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

• GitHub: https://github.com/langchain-ai/langchain ⭐ 119,000+
• Docs: https://docs.langchain.com
• API Reference: https://reference.langchain.com/python
• LangSmith: https://smith.langchain.com (observability)
• Version: 0.3+ (stable)
• License: MIT

Agent operating procedure

1. Check the environment. Confirm the framework version, model provider, API keys and rate limits.
2. Pin down the inputs. Confirm formats, identifiers and parameters from the data or the user. Ask rather than guess any value that changes the result.
3. Run a small version first. Test a single call or chain with a known input and inspect raw outputs.
4. Execute the full task using the instructions and references above.
5. Validate the result. Evaluate on a small labeled set; check structured outputs against their schema; log prompts and responses.
6. Report. State what was run (versions, commands, parameters), what was checked, and what is still uncertain.

If this happens	Do this
Outputs do not match the expected schema	Add validation and retries, tighten the schema, or simplify the prompt.
A function, flag or endpoint in these instructions is missing in the installed version	Check the installed version's own documentation (help(), --help, official docs), adapt, and tell the user. Never invent an API.
A required input, identifier or parameter is ambiguous	Ask the user, or state the assumption explicitly before running.

Integrity rules

• Never fabricate results, parameters, identifiers, citations or statistics. If something cannot be run or verified, say so plainly.
• Never send private or sensitive data to external APIs without the user's consent.
• Treat version-specific details here as possibly outdated: confirm them against the official documentation for the installed version.
• Ask before actions that cost money, consume shared GPUs or cloud quota, touch personal or patient data, or cannot be undone.

Related skills

• llamaindex: Data framework for building LLM applications with RAG.
• autogpt-agents: Autonomous AI agent platform for building and deploying continuous agents.
• chroma: Open-source embedding database for AI applications.